Sage Journals: Discover world-class research

Abstract

Contributors

The following document and appendices represent the third edition of the Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy. These guidelines were developed by the Diabetic Retinopathy Telehealth Practice Guidelines Working Group. This working group consisted of a large number of subject matter experts in clinical applications for telehealth in ophthalmology.

The editorial committee consisted of Mark B. Horton, OD, MD, who served as working group chair and Christopher J. Brady, MD, MHS, and Jerry Cavallerano, OD, PhD, who served as cochairs. The writing committees were separated into seven different categories. They are as follows:

Clinical/operational: Jerry Cavallerano, OD, PhD (Chair), Gail Barker, PhD, MBA, Christopher J. Brady, MD, MHS, Yao Liu, MD, MS, Siddarth Rathi, MD, MBA, Veeral Sheth, MD, MBA, Paolo Silva, MD, and Ingrid Zimmer-Galler, MD.

Equipment: Veeral Sheth, MD (Chair), Mark B. Horton, OD, MD, Siddarth Rathi, MD, MBA, Paolo Silva, MD, and Kristen Stebbins, MSPH.

Quality assurance: Mark B. Horton, OD, MD (Chair), Seema Garg, MD, PhD, Yao Liu, MD, MS, and Ingrid Zimmer-Galler, MD.

Glaucoma: Yao Liu, MD, MS (Chair) and Siddarth Rathi, MD, MBA.

Retinopathy of prematurity: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD.

Age-related macular degeneration: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD.

Autonomous and computer assisted detection, classification and diagnosis of diabetic retinopathy: Michael Abramoff, MD, PhD (Chair), Michael F. Chiang, MD, and Paolo Silva, MD.

CONTRIBUTORSߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ495

PREAMBLEߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ496

SCOPEߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ497

INTRODUCTIONߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ497

PRACTICE GUIDELINESߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

Clinical guidelinesߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

I. Principles of an ocular telehealth program for DRߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

A. Missionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

B. Visionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

C. Goalsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

D. Guiding principlesߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

II. Ethicsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

III. Clinical validationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ498

A. Category 1ߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ499

B. Category 2ߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ499

C. Category 3ߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ500

D. Category 4ߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ500

IV. Communicationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

V. Personnel qualificationsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

A. Medical care supervisorߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

B. Patient care coordinatorߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

C. Image acquisition personnelߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

D. Image review and evaluation personnelߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

E. Information systems personnelߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

Technical guidelinesߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

I. Equipment specificationsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ501

A. Image acquisitionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ503

B. Image displayߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ503

C. Image analysisߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

II. Data managementߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

A. Interoperabilityߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

B. Compressionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

C. Data communication and transmissionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

D. Archiving and retrievalߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ504

E. Securityߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

F. Reliability and redundancyߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

G. Documentationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

Administrative guidelinesߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

I. Legal requirementsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

A. Facility accreditationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

B. Health Insurance Portability and Accountability Actߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

C. Privileging and credentialingߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

D. Fraud and abuseߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ505

E. State medical practice acts/licensureߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ506

F. Tort liabilityߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ506

G. Consentߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ507

II. Quality controlߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ507

III. Operationsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ507

IV. Customer supportߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ507

A. Originating siteߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ507

B. Data transmissionߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

C. Distant siteߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

V. Financial factorsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

A. Reimbursementߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

B. Grantsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

C. Federal programsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

D. Other financial factorsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ508

E. Equipment costߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ509

TABLES AND FIGURESߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ499,502,503,512,514,520,522

ABBREVIATIONSߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ509

GLOSSARYߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ509

APPENDICESߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ510

1. Clinical Validationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ510

2. Interoperabilityߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ511

3. Autonomous and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathyߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ513

4. Health Insurance Portability and Accountability Actߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ519

5. Privileging and Credentialingߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ519

6. Quality Controlߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ520

7. Customer Supportߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ521

8. Reimbursementߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ522

9. Telemedicine for Glaucoma: Guidelines and Recommendationsߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ524

10. Telemedicine for Retinopathy of Prematurityߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ527

11. Telemedicine for Age-Related Macular Degenerationߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ533

REFERENCESߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙߙ535

Preamble

The American Telemedicine Association (ATA) brings together diverse groups from traditional medicine, academia, technology, telecommunications companies, e-health, allied professional and nursing associations, medical associations, government, military, regulatory, and other stakeholders to address and advance compliance with legal, ethical, and professional standards in the practice of telemedicine. The ATA has embarked on an organized effort to establish guidelines for the practice of telemedicine in various clinical applications to define patient and provider expectations, aspire to uniform quality of service for patients and providers, enhance patient experience, and enable providers to deliver appropriate care using evidence-based practices.

The guidelines are developed by panels that include experts from the field and other strategic stakeholders, and are designed to serve as a standard reference and educational tool for professionals using telehealth tools for health care service delivery. The process for developing these guidelines is based on evidence, professional consensus, and a rigorous review, including open public commentary period, with final approval by the ATA Board of Directors. Guidelines are reviewed and updated periodically.

The purpose of these guidelines is to assist providers in pursuing a sound course of action in providing safe and effective medical care using telehealth tools based on current scientific knowledge, technological requirements, and patient needs. Safe and effective practice requires technical training, professional knowledge and skill, and explicit processes as described in each document.

Adherence to these guidelines alone will not guarantee accurate diagnoses, appropriate clinical treatment, or optimal outcomes. Appropriate divergence from the guidelines may be indicated under certain conditions, such as emergency situations or locations with limited resources or other unavoidable constraints. Similarly, technological advances may alter prevailing practices or provide new and expanded opportunities.

The guidelines in this document are based on the accumulated knowledge and experience of the ATA workgroups, eye care and telemedicine professionals, and other stakeholders, and generally describe the evidenced-based best practices for ocular telehealth. However, the technical and administrative guidelines do not purport to establish binding legal standards for delivering telemedicine services.

The previous ATA Ocular Telehealth Diabetic Retinopathy Practice Guidelines were issued in 2011. This third edition reflects new evidence, new technologies, and expanded scope of the ocular telehealth domain. All guidelines issued by the ATA are properties of the ATA. Any modification or reproduction of the published guidelines must receive prior approval by the ATA.

Scope

The following document includes fundamental requirements to be followed when providing medical and other health care services using telecommunication technologies, and any other electronic communications between patients, practitioners, and other health care providers, as well as “best practice” recommendations. The guidelines apply to individual practitioners, group and specialty practices, hospitals and health care systems, and other providers of health-related services where there are telehealth interactions between patients and health care service providers.

When guidelines, position statements, or standards from any other professional organization or society exist, health professionals should also review these documents and, as appropriate, incorporate them into practice.

These guidelines pertain primarily to health care professionals and patients located in the United States. In situations wherein either or both parties are not within the United States, these guidelines may be referenced, but any local guidelines that are in place should take precedence.^1
–3

These guidelines are intended to be used as a companion to the ATA Core Operational Guidelines for Telehealth Services.⁴ Recommendations in the core guidelines are not repeated herein except to emphasize or expand upon a particular point, or to provide domain-specific detail. The reader should review the core guidelines first to provide the context for proper understanding and implementation of the Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy.

The guidelines address three aspects of service delivery: clinical, technical, and administrative. Based upon the quantity and quality of peer-reviewed evidence, the guidelines are classified into four levels of adherence:

“Shall” indicates required action whenever feasible and/or practical.

“Shall not” indicates a proscription or action that is strongly advised against.

“Should” indicates a recommended action without excluding others.

“May” indicates pertinent actions that may be considered to optimize the telemedicine encounter or operational process.

These indications are found in bold throughout the document.

Introduction

These guidelines present recommendations for designing, implementing, and operating an ocular telehealth diabetic retinopathy (DR) program in a broad range of clinical settings and targeted outcomes. This document also addresses current clinical, technical, and administrative issues that form the basis for evaluating DR telehealth techniques and technologies. These guidelines are intended to be consistent with federal regulations and industry best practices at the time of publication that emphasize clinical quality, data security and integrity, and interoperable health information exchange. Federal, state, and regional regulations supersede the recommendations in these guidelines. This document will be reviewed periodically and revised to reflect evolving technologies, evidence, regulations, and clinical guidelines.

This third edition of the guidelines includes four new clinical appendices that introduce additional ocular telehealth domains (Appendices A3, A9–A11). These are planned for future development into independent guidelines to be included in an ocular telehealth suite of practice guidelines.

Practice Guidelines

Clinical Guidelines

I. Principles of an ocular telehealth program for DR

Private individuals, public and private organizations, and national and international agencies may undertake telemedicine programs for DR that have been shown to be efficacious, cost-effective, and scalable means to identify diabetes-related eye disease and thereby prevent visual loss. Designing, building, implementing, and sustaining an ocular telehealth DR program require clearly defined mission, vision, goals, and guiding principles. The following statements are a guide for leadership and staff in developing and sustaining appropriate and effective programs.

A. Mission

Increase cost-effective and culturally sensitive access and adherence to accepted standards of eye care for people with diabetes mellitus (DM).

B. Vision

Ocular telehealth can be an integral component of primary care for people with DM by expanding patient-centric access to retinal examinations consistent with evidence-based recommendations for eye care in diabetes.

C. Goals

Improve access to diagnosis and evidence-based management of DR.

Reduce the incidence of vision loss due to DR.

Decrease the cost of identifying patients with DR.

Promote telehealth to enhance the efficiency and clinical effectiveness of evaluation, diagnosis and management of DR.

Promote telehealth to enhance the availability, patient centricity, quality, efficiency, and cost-effectiveness of remote evaluation and management of DR.

Facilitate integration of diabetes eye care with primary and specialty medical care.

Promote widespread adoption of telehealth services for DR.⁵

D. Guiding principles

Although ocular telehealth programs offer new opportunities to improve access and quality of care for people with DR, programs shall be developed for deployment in a safe and effective manner. Program outcomes shall be closely monitored to meet or exceed current standards-of-care for retinal examination and identify opportunities to improve service delivery and clinical outcomes.

DM adversely affects the entire eye and has a diverse influence on visual function. Patients should be aware that a validated teleophthalmology examination of the retina may substitute for a traditional onsite dilated retinal evaluation for DR, but patients shall be informed that the examination is not a replacement for a comprehensive eye examination, and does not replace the need for ongoing care by conventional eye examinations.

II. Ethics

Regardless of the program, the care of the patient shall not be compromised. Telemedicine practice shall conform to the same professional ethics that govern in-person care. This responsibility encompasses a broad range of issues including, but not limited to, confidentiality, image quality, data integrity, clinical accuracy, reliability, and adherence to all applicable national and local regulations such as Health Insurance Portability and Accountability Act (HIPAA). Telemedicine programs and providers shall incorporate ethical statements and policies and legal/regulatory requirements into their standard operating procedures, including:

An explicit code of ethics.

Compliance with applicable federal, state, and jurisdictional laws and regulations, and institutional policies.

Nondiscrimination clause regarding denial of service to people on the basis of disability, gender, gender preference or sexual orientation, ethnicity, national origin, or religious affiliation.

Provision of care in a culturally sensitive manner.

Provision of service not conditional upon receipt of payment by the patient.

III. Clinical validation

Multicenter national clinical trials provide evidence-based criteria for clinical guidelines in diagnosing and treating DR ( Appendix A1). Telehealth programs for DR shall define program goals and performance in relation to broadly accepted clinical standards.

Early Treatment Diabetic Retinopathy Study (ETDRS) 30°, stereoscopic, seven-standard field, color, 35 mm slides (based on the ETDRS extension of the modified Airlie House classification of DR,⁶ ETDRS photographs) have been the gold standard for evaluating DR in major clinical trials of DR. Although no standard criteria have been widely accepted as performance measurements of digital imagery used for DR evaluation, current clinical trials sponsored by the National Eye Institute have transitioned to digital images for DR assessment.^7,8

Telehealth programs for DR should demonstrate an ability to compare favorably with ETDRS film or digital photography as reflected in kappa values for agreement of diagnosis, false-positive and false-negative readings, positive predictive value, negative predictive value, sensitivity and specificity of diagnosing levels of DR and diabetic macular edema (DME).^9

–12 Because programs have reported referral-warranted ocular disease in many patients with ungradable images, inability to obtain or grade images should be considered a positive finding and patients with unobtainable or ungradable images should be promptly reimaged or referred for a more advanced evaluation.¹³

It is recognized that severity levels of DR other than those defined by the ETDRS are used clinically for grading DR (see Table 1 for comparisons between ETDRS levels of DR and the International Clinical Diabetic Retinopathy Disease Severity Scale, and Table 2 for comparisons between ETDRS DME and the International Clinical Diabetic Retinopathy Disease Severity Scale).¹⁴ Protocols should state the reference standard used for validation and relevant data sets used for comparison.

Table 1.

International Clinical Diabetic Retinopathy Scale Compared with Early Treatment Diabetic Retinopathy Study Levels of Diabetic Retinopathy

INTERNATIONAL CLASSIFICATION LEVEL OF DR	ETDRS LEVEL OF DR (ETDRS REPORT 12)
No apparent retinopathy	Level 10; DR absent
No apparent retinopathy	Levels 14, 15; DR questionable
Mild NPDR	Level 20; microaneurysms only
Moderate NPDR	Level 35; mild NPDR
Moderate NPDR	Levels 43, 47; moderate NPDR
Severe NPDR	Levels 53A–53E; severe NPDR, very severe NPDR
PDR	Level 61; mild PDR
	Level 65; moderate PDR
	Levels 71, 75; high-risk PDR
	Levels 81, 85; advanced PDR

DR, diabetic retinopathy; ETDRS, Early Treatment Diabetic Retinopathy Study; NPDR, nonproliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy.

Table 2.

International Clinical Diabetic Macular Edema Scale Compared with Early Treatment Diabetic Retinopathy Study Where Noted

DISEASE SEVERITY LEVEL	FINDINGS	DME SCALE
DME apparently absent	No apparent retinal thickening or HE in posterior pole
DME apparently present	Some apparent retinal thickening or HE in posterior pole	Mild DME: some retinal thickening or HE in posterior pole but distant from center of the macula (ETDRS: DME but not CSME) (not central-involved DME)
		Moderate DME: retinal thickening or HE approaching the center but not involving the center (ETDRS: CSME) (not central-involved DME)
		Severe DME: retinal thickening or HE involving the center of the macula (ETDRS: CSME) (central-involved DME)

CSME, clinically significant macular edema; DME, diabetic macular edema; HE, hard exudates.

This Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy defines four major categories of validation for DR telehealth programs using ETDRS photographs as the reference standard. The validation study shall be structured to assess the program's “end-to-end” performance rather than any single piece of its technology, and the study design should follow conventional scientific methodology. Although ETDRS photographs currently provide an ideal standard for validation, clinical comparators may be used for program validation if the examination is conducted by a retinal specialist using accepted best practices.

Validation categories are not a quality continuum, but rather performance categories that describe distinct clinical outcomes of public health relevance reflecting program goals. In addition, they provide a standardized language for communicating performance for clinical, research, reimbursement, request for proposal (RFP), and regulatory compliance purposes. Information about the program's validation study design and performance should be publicly available to users and other stakeholders.

A. Category 1

Category 1 validation indicates a system can separate patients into one of two groups: (1) those who have no or very mild nonproliferative diabetic retinopathy (NPDR) (ETDRS level 20 or below) and (2) those with levels of mild NPDR or greater (greater than or equal to ETDRS level 35). Functionally, category 1 validation allows screening for presence versus absence of DR.

B. Category 2

Category 2 validation indicates a program accurately determines if sight-threatening diabetic retinopathy (STDR) or potentially STDR is present or not present as evidenced by any level of DME, severe or worse levels of NPDR (ETDRS level 53 or worse), or proliferative diabetic retinopathy (PDR) (ETDRS level 61 or worse). Functionally, category 2 allows screening for presence versus absence of STDR or potentially STDR.

C. Category 3

Category 3 validation indicates that a program accurately identifies ETDRS-defined clinical levels of NPDR (mild, moderate, or severe), PDR (early and high risk), and DME (central-involved DME or not central-involved DME). Functionally, category 3 validation provides a clinical diagnosis of DR/DME severity to match conventional clinical retinal examination through dilated pupils or ETDRS photographs, allowing remote management of the patient.

D. Category 4

Category 4 validation indicates that a program accurately identifies the presence and degree of specific lesions of DR to match the ability of ETDRS photographs to determine all specific lesions and levels of DR and DME, ranging from levels 10 to 90. Functionally, category 4 validation indicates a program can replace or coexist with ETDRS photographs as a gold standard and may be used in any clinical or research program.¹⁵

The validation categories entail all components of a program (end-to-end) and do not refer to any single element such as the retinal imaging device, imaging protocol, image manager, compression protocol, image display, and image review protocol.

Determination of the validation category should be done by a properly designed study using ETDRS photographs as controls, although clinical comparators by a retinal specialist may be used. The study groups shall include statistically appropriate representation from the full range of DR and DME severity from no clinically evident DR/DME to PDR and central-involved DME or not central-involved DME.

Threshold sensitivity and specificity for validation categories 1 and 2 shall be 80% and 95%,¹⁶ respectively, and shall be calculated including ungradable images. For categories 3 and 4, a test of categorical agreement such as the kappa statistic with substantial agreement should be used. For example, the system of Landis and Koch defines slight agreement as kappa of 0–0.20; fair agreement, 0.21–0.40; moderate agreement, 0.41–0.60; substantial agreement as 0.61–0.80; and almost perfect agreement as >0.81.^11,17 The threshold for image gradability shall be defined in a structured manner, and ungradable images shall be included as a positive finding in statistical analyses.

The study that establishes the program's clinical performance and validation category applies to all its implementations. Clinical fidelity with the validation study is maintained by standardized implementation and ongoing quality assurance (QA) ( Appendix A6). Accordingly, individual implementations within the original program need not be restudied. However, substantial changes in technology or clinical operations may warrant repeat study to re-establish clinical performance and validation category.

A telehealth program's validation category impacts clinical, business, and operational features. The category influences hardware and software technology, staffing and support, clinical workflow and outcomes, participant licensure, QA, and business plan. Equipment cost, technical difficulty, operational complexity, and training requirements increase with increasing program performance as measured by validation category.¹⁸

A telehealth program's goals and desired performance may influence choice of technology and protocol. Some programs use pharmacologic pupil dilation on all or selected patients, whereas others perform imaging with nonmydriatic cameras and undilated pupils. A higher rate of ungradable photographs has been reported through undilated versus dilated pupils.^19
–21 People with diabetes, particularly those >50 years of age, often have smaller pupils and a greater incidence of cataracts, which may limit image quality if performed through an undilated pupil.^22,23

Pupil dilation is associated with a small risk of angle-closure glaucoma. Although the risk of inducing angle-closure glaucoma with dilation using 0.5% tropicamide is minimal with no reported cases in a large meta-analysis,^24,25 programs using pupil dilation shall have a defined protocol to recognize and address this potential complication. Pupil dilation is not an operational requirement for any particular validation category, but ocular telehealth programs for DR may use pupil dilation based upon regulatory dependencies, program preferences, and outcome goals.

Depending on the telehealth program operational preferences and validation category, images may be acquired and reviewed stereoscopically. Evidence suggests that accurate identification of macular edema presence or severity may not always be possible using nonstereoscopic modalities.²⁶ Without direct assessment of retinal thickening through stereoscopic evaluation or optical coherence tomography (OCT), determination of DME relies upon surrogate lesions of hard exudates or microaneurysms in the macular field.^27,28 However, macular edema is not completely defined or identifiable by these surrogate markers in all cases.^29,30 Central-involved DME or not central-involved DME is often accompanied by other DR lesions that may also independently trigger referral.

It is possible that a program without stereoscopic capabilities or OCT may be validated to identify macular edema with acceptable sensitivity,^28,31 even though stereoscopic evaluation of DME is significantly more sensitive and specific than monoscopic techniques.²¹ Artificial intelligence (AI) algorithms may offer another indirect measure of DME that has sufficient accuracy to warrant clinical applications in some settings³² (Appendix A3). A program may use nonstereoscopic techniques to establish DME severity based upon its operational preferences and demonstrated validation category.

IV. Communication

Communication is the foundation of ocular telehealth.^33,34 Communication shall be coordinated and reliable among originating site (OS) and distant site (DS), telehealth providers and patients, and telehealth providers and other members of the patient's health care team. Communication with patients shall be aligned with the patients' cultural and physical needs. Providers interpreting retinal telehealth images shall render reports in accordance with relevant jurisdictions, community standards, and regulatory requirements. Although reporting is typically provided to referring providers, the program should emphasize consistent and timely communication to the patient of the telemedicine examination outcome.

V. Personnel qualifications

Telehealth programs for DR depend upon a variety of functions. Distinct individuals may assume these responsibilities or a person may assume several roles depending on the size and scope of the program. Qualifications of these personnel shall be documented, including initial and recurrent training.

A. Medical care supervisor

An appropriately licensed ophthalmologist or optometrist with expertise in evaluation and management of DR shall assume ultimate responsibility for the program and is responsible for oversight of image interpretation, report recommendations, and patient safety. Responsibilities include delivering timely recommendations for appropriate care management and providing feedback to the imagers, graders, and other program participants. Responsibilities also include ensuring that all components of the program, including image acquisition, grading, and reporting, are of appropriate quality and that related patient health data meet accepted and expected standards. Nonmedical oversight may be used depending on validation category, goals of the program, regulatory requirements, and QA safeguards. This role may include coordinating and tailoring the integration of the telehealth workflow for DR into the local clinical setting.³⁵

B. Patient care coordinator

The patient care coordinator ensures that each patient receives DR education and completes appropriate follow-up, especially for those meeting criteria for referral. A program may use a dedicated position for this role or use a shared position depending on the program size and geographic scope.

C. Image acquisition personnel

Image acquisition personnel (“imagers”) are responsible for acquiring retinal images. A licensed eye care professional may not be physically available at all times during a telehealth session, so imagers shall possess the knowledge and skills for imaging independently or with assistance and consultation by telephone, including:

Understanding of basic ocular telehealth technology and principles.

Qualifications for obtaining appropriate image fields of diagnostic quality.

Understanding of the clinical appearance of common retinal diseases requiring immediate or urgent evaluation.

Communication skills for acquiring patient informed consent and providing patient education.

Basic understanding of angle-closure glaucoma if pupil dilation is performed, including entry-level skills in screening for shallow anterior chamber and recognition of angle-closure signs and symptoms.

D. Image review and evaluation personnel

Image review and evaluation specialists (readers) are responsible for timely grading of images for retinal lesions and determining levels of DR. Only qualified readers shall perform retinal image grading and interpretation. Qualifications shall include academic and clinical training. If a reader is not an optometrist or ophthalmologist, specific training and demonstrated proficiency shall be required. Grading skills shall be appropriate to technology and ATA validation category used in the ocular telehealth DR program.

A licensed qualified optometrist or ophthalmologist with expertise in DR and familiarity with program technology should supervise readers. An adjudicating reader may resolve ambiguous or controversial interpretation. In most cases, an adjudicating reader may be an optometrist or ophthalmologist, but in all cases the adjudicating reader shall have special qualifications in DR by training or experience.

E. Information systems personnel

An information systems specialist is responsible for system privacy/confidentiality protocols, connectivity, data integrity, availability of stored images, and disaster recovery.^36,37 The specialist should be available in case of system malfunction to solve problems, initiate repairs, and coordinate system-wide maintenance.

Technical Guidelines

I. Equipment specifications

Telehealth systems used in the United States shall conform to applicable Food and Drug Administration (FDA) regulations. Telehealth systems used inside and/or outside the United States should meet applicable national and local statutes, regulations, and accepted standards. Elements include:

Image acquisition hardware (computers, cameras, and other peripherals).

Image transmission, storage and retrieval, and display systems.

Image analysis and clinical workflow management (scheduling follow-up examinations, clinical communication management, and decision support tools).

Security and confidentiality of protected health information (PHI) and images.

Equipment specifications will vary with program needs, validation category, and available technology (Fig. 1). Equipment shall provide image quality and availability appropriate for clinical needs, program goals, and regulatory requirements. The diagnostic accuracy of any imaging system shall be validated as an integrated component of the overall program before incorporation into a telehealth system.^{10
–12,38–42} Specific imaging and reading technology and protocols vary widely, but are generally related to the operational environment and validation category.

Fig. 1.

IHE-Eye Care, unified eye care workflow (Reprinted with the permission of the IHE Eye Care Domain). EMR, electronic medical record; IHE, Integrating the Healthcare Enterprise; PMS, patient management system.

All relevant technologies, including image acquisition, image management/Picture Archiving and Communication System (PACS), and interfaces to patient management systems (PMSs), and electronic medical records (EMRs)/electronic health records (EHRs), should be Digital Imaging and Communications in Medicine (DICOM)⁴³ and Health Level 7 (HL7) standards compliant. New equipment and periodic upgrades to incorporate expanded DICOM standards should be part of an ongoing performance improvement program. DICOM Supplement 91 (Ophthalmic Photography), which addresses ophthalmic digital images, was released in 2004 and updated in 2009,⁴⁴ DICOM Supplements 110 (Ophthalmic Tomography) 2007,⁴⁵ 173 (Wide Field Ophthalmic Photography) 2015,⁴⁶ and 197 (Ophthalmic Tomography Angiography) 2017⁴⁷ may be useful in certain ocular telehealth applications and should be considered if relevant technology is used.

A. Image acquisition

To provide alignment with the accepted standards for medical imaging, retinal image data sets should adhere to DICOM standards. When DICOM protocols are used, patient information, eye and retina characteristics, image type, type of retinal examination, retinal image set, and other data shall be linked to image files as metadata.⁴⁸ Additional relevant information such as medical and surgical history, and laboratory values may also be included as metadata of an image series or otherwise linked to the images for use during image interpretation and reporting ( Appendix A2).

There are many equipment options available for image capture, but most devices currently used in telemedicine for DR (Tmed-DR) are flash-based fundus cameras designed for eye clinic settings and adapted for telemedicine use.²¹ The device selected shall be appropriate for the program's clinical, business, and operational characteristics, and shall be used in a manner suitable for the validation category, and coordinated with the other equipment components of the program (see Interoperability section and Appendix A2).

Many factors must be considered when selecting a particular retinal imaging device and imaging protocol. Most commercially available retinal imaging devices have sufficient resolution for Tmed-DR. The minimum resolution for this purpose is 20 pixels per degree.⁴⁹ Diagnostic accuracy of the system is the pivotal feature that enables a particular validation category. The ungradable rate is a related feature since this rate can affect the system's functional specificity.

Important features influencing diagnostic accuracy include field of view (FOV) and mydriatic versus nonmydriatic imaging.²¹ Although variation in methodology makes it difficult to compare existing reports, in general, larger aggregate FOV and mydriasis are associated with the highest diagnostic accuracy and lowest ungradable rate when using flash photography. (Table 3) The total FOV is the most influential feature in this consideration, with nonmydriatic ultrawide field imaging performance roughly equivalent to multifield mydriatic systems.²¹

Table 3.

Meta-analysis of a 20-Year Review of the Telemedicine for Diabetic Retinopathy Literature

NO. OF 45° FIELDS	SENSITIVITY (%)		SPECIFICITY (%)		UNGRADABLE RATE (%)
NO. OF 45° FIELDS	MYDRIATIC	NONMYDRIATIC	MYDRIATIC	NONMYDRIATIC	MYDRIATIC	NONMYDRIATIC
1	83% ± 11	72% ± 14	88% ± 17	95% ± 4	8% ± 7	24% ± 20
2	81% ± 18	82% ± 31	92% ± 6	77% ± 23	4% ± 3	19% ± 10
3	89% ± 6	87% ± 16	93% ± 7	91% ± 14	5% ± 3	15% ± 12

The predominate format of 45° FOV systems are shown. Mean sensitivity and specificity to match program goals (diabetic retinopathy detection, severity, or referral rate) and study ungradable rate (± standard deviation) of 45° FOV systems using 1, 2, or 3 mydriatic or nonmydriatic 45° fields.

Note: Adapted from Horton et al.²⁴

FOV, field of view.

The form factor of the imaging station (retinal camera and supporting equipment) is an early consideration during equipment selection. A system that can be easily transported between sites allows an increased and adaptable catchment area for the program while limiting equipment costs. Most retinal imaging devices for this purpose must be adapted from devices designed and marketed for conventional clinic applications.

Mobile systems based upon a smartphone platform have a favorable form factor and cost features, and carry the additional advantage of integrated image transmission. Although clinical potential has been demonstrated with these devices,⁵⁰ limited sensitivity and specificity for DR detection and severity level diagnosis limit their use. Moreover, a lack of standardization and a short product cycle life create significant business and interoperability challenges.⁵¹

Portable systems using handheld imaging devices are larger and more costly than smartphones, and may suffer from some of the same limitations.^52
–54 High-quality evidence of their efficacy is lacking, although studies are ongoing to validate these devices.⁵⁵

Another alternative for portable Tmed-DR operations is the conversion of a conventional fundus camera for portable use by use of a transportable case. This method retains the performance and connectivity benefits of the conventional retinal imager but often requires the construction of a customized hardened case for device protection, resulting in a large and heavy item that may be cumbersome to move, and requires a desktop configuration.

B. Image display

Retinal images used for diagnosis should be displayed on high-quality monitors of appropriate size, resolution, gamma setting, refresh rate, and viewing environment. Monitors, stereoscopic viewing (if applicable), and settings should be appropriate for the program's clinical goals, and described in its validation study. Displays should be calibrated regularly to ensure ongoing fidelity with original validation display conditions. Revalidation should be performed if settings or components are materially changed. Ambient light level, reflections, and other artifacts should be controlled in the reading area to ensure standardized viewing consistent with the original validation conditions.

C. Image analysis

Computer algorithms to enhance digital retinal image quality or provide automated identification of retinal pathology are emerging technologies. Image analysis tools for enhancing image quality (histogram equalization, edge sharpening, image deconvolution, etc.) or identifying lesions such as microaneurysms, hemorrhages, or hard exudates can be used to aid retinopathy assessment. Computer algorithms may also be used to facilitate and standardize reader assessment of DR and DME severity using rules based upon accepted standards. Appendix A3 summarizes the use of autonomous and computer-assisted detection for classification and diagnosis of DR image processing.

Computer algorithms for DR assessment of retinal images shall undergo rigorous clinical validation with the outcome mapped to the ATA validation categories for DR before being used. Regulatory approval may be required in the United States.

The nature of telemedicine allows clinical and related patient data to be reviewed remotely in a nonclinical setting where ambient conditions and privacy are less controlled. Staff involved in assessment of Tmed-DR images and related data shall ensure privacy and confidentiality of all patient information. The reading environment shall be reasonably controlled for reader distractions, and the ambient lighting shall be consistent with monitor calibration.

II. Data management

A. Interoperability

Health information technology (HIT) interoperability is the ability of systems to exchange and use electronic health information from other systems without special effort on the part of the user to advance the health status of and the effective delivery of health care for individuals and communities.⁵⁶ HIT interoperability has been recognized as a key element in moving the health care system toward improved outcomes, patient safety, and efficiencies.⁵⁷

In the United States, an integrated digital health care system has been described by federal regulations and its implementation heavily incentivized. Initially these incentives occurred through supplemental payments, but more recently this approach has transitioned to a system of financial penalties for nonconforming providers and health care facilities. This emphasis stems from evidence that harmonized communication of HIT improves operational efficiency, patient safety, and public health reporting through the availability of patient health information at the right place and the right time. The current regulatory roadmap suggests continued regulatory attention to interoperability,^58,59 so ocular telehealth programs should consider interoperability options when selecting equipment and software. Additional information about interoperability is available in Appendix A2.

B. Compression

Data compression may facilitate efficient transmission, storage, and retrieval of retinal images, and may be used if the algorithms have undergone clinical validation.^60,61 DICOM recognizes lossy and lossless compression of medical images in multiple supplements relevant to ocular telehealth, and the type and character of compression used are encoded in the DICOM metadata.^44,45,62,63 Compression types and ratios shall be included in clinical validation and should be periodically reviewed to ensure appropriate clinical image quality and diagnostic accuracy.

C. Data communication and transmission

A variety of technologies are available for data communication. Ocular telehealth programs should determine specifications for transmission technologies best suited to the program's clinical, technical, and business needs. Transmission systems shall have robust error checking and recovery protocols to ensure data integrity.⁶⁴ Data communications should be compliant with DICOM and HL7 standards. If DICOM conformant equipment is used, ocular telehealth system equipment manufacturers shall supply DICOM conformance statements.

If ocular telehealth applications are integrated with existing health information systems, interoperability should incorporate DICOM and HL7 conformance, and establish appropriate workflow for patient scheduling and report transmission.⁶⁵ Integrating the Healthcare Enterprise-Eye Care (IHE-Eye Care) Technical Frameworks⁶⁶ may be used to further facilitate and standardize health information exchange between imaging devices and EHRs.

D. Archiving and retrieval

Ocular telehealth systems shall provide storage capacities and duration in compliance with facility, state, and federal medical record retention regulations. Images may be securely stored and archived locally, at imaging or reading sites, offsite, or on the web, and shall satisfy all jurisdiction requirements. Storage and query/retrieve transactions with PACS or other image mangers should conform to DICOM protocols. All study images and reports shall be available consistent with regulations and statues.

Each facility shall have digital image archiving policies and procedures equivalent to existing policies for protecting other data and hardcopy records. Telehealth programs shall also address HIPAA security requirements for data backup and archive.

E. Security

Ocular telehealth systems shall have network and software security protocols to protect patient confidentiality and identification of image data. Measures shall be taken to safeguard and ensure data integrity against intentional or unintentional data corruption. Privacy should be ensured through a minimum 128-bit encryption and two-factor authentication technology. Digital signatures may be used at image acquisition sites. Transmission of retinal imaging studies and study results shall conform to HIPAA privacy and security requirements.

F. Reliability and redundancy

Written policies and procedures shall be in place to ensure continuity of care and conformance to HIPAA requirements at levels similar to that for hardcopy retinal imaging studies and medical records. Policies and procedures should include internal redundancy systems, backup telecommunications, and a disaster recovery plan. Ocular telehealth reports shall be retained and digital retinal images should be retained as part of patient medical records in a manner and duration to meet regulatory, facility, and medical staff clinical needs.

G. Documentation

Readers rendering reports on DR or other ocular abnormalities should comply with standardized diagnostic and management guidelines as established by the American Academy of Ophthalmology⁶⁷ or the American Optometric Association.⁶⁸ Reports should be based on HL7 or DICOM formats to facilitate health information exchange and recognition by quality performance surveys. Reports should provide DR severity levels consistent with accepted standards as appropriate for ATA validation category used. Medical nomenclature should conform to Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT)⁶⁹ standards. Transmission of reports shall conform to HIPAA privacy and security requirements.

Administrative Guidelines

I. Legal requirements

Legal and regulatory issues relating to the practice of ocular telehealth are generally the same as other telemedicine modalities and carry the risk management considerations of conventional medical practice.^33,70,71 A DR telehealth program should use the same safeguards to mitigate risk.

A. Facility accreditation

Some hospital telehealth programs fall within regulatory jurisdictions of The Joint Commission (TJC) and/or Centers for Medicare and Medicaid Services (CMS).⁷² TJC and Accreditation Association for Ambulatory Health Care accredit ambulatory health care facilities.^73,74 These accrediting bodies publish standards that apply to telemedicine activities, making regulatory compliance a mandatory component for most hospital-based telehealth programs. There are specific references to telemedicine in TJC Environment of Care and Medical Staff sections, including LD.04.03.09, MS.13.01.01, and MS.13.01.03.⁷⁵

CMS requirements also occur indirectly through related activities, such as standards for contract care. There are other accreditation standards that may apply to a specific program and clinical setting, with similar, but not identical requirements. Awareness and understanding of these standards and the applicable CMS regulations can be daunting.⁷⁶ Ocular telehealth programs shall carefully review applicable standards to ensure conformance.

B. Health Insurance Portability and Accountability Act

Ocular telehealth programs should obtain professional consultation for HIPAA compliance specific to their program. Telehealth programs shall consider HIPAA privacy^77,78 and security^79,80 regulations in clinical, administrative, and technical operation plans. Privacy and security issues are listed in Appendix A4.

C. Privileging and credentialing

Ocular telehealth providers may require privileging and credentialing. Licensed providers responsible for interpretation of retinal telehealth images shall be credentialed and obtain privileges at OS and DS if required by applicable statues and regulations, and facility bylaws.^81,82 Technical staff usually do not require formal privileging and credentialing, but shall have their duties and job-specific competencies described in a position description or equivalent. If telemedicine providers undergo credentialing and privileging, ocular telehealth programs should utilize the CMS regulations and accreditation standards for “privileging and credentialing by proxy.” See Appendix A5 for CMS regulations and accreditation standards for telemedicine providers.

D. Fraud and abuse

Telemedicine programs are subject to the fraud and abuse statutes and regulations concerning health care-related kickbacks and other financial inducements for referrals. The antikickback statute prohibits payment or any receipt of remunerations for referrals or purchasing equipment reimbursable under federal health programs.⁸³ The language in this law is so broad that “Safe Harbors” were created to lessen the impact on legitimate ventures.⁸⁴

The Stark Act prohibits physicians from making a referral for designated health services to an entity with which the physician (or immediate family member) has a financial relationship.^85,86 Self-referrals occur when physicians refer patients to medical facilities in which they or their immediate family have a financial interest. For example, an ophthalmologist places a retinal imaging workstation in a primary care provider's office at deep discount or gratis and reads images at little or no charge. The Stark statute may have been violated if patients needing treatment are referred to the ophthalmologist. This practice may be avoided by charging the primary care provider full market value for equipment and services and offering the patient a choice of referral ophthalmologists for treatment.⁸⁷

Ocular telehealth programs should obtain council to establish policies and operational practices that prevent violation of the antikickback laws and Stark Act.

Another area of risk under the general category of fraud and abuse is antitrust. Although telemedicine and other e-health practices offer the opportunities of improved business efficiencies, reduced incremental costs of services, and new product offerings, in certain settings they may also be interpreted as restraining trade. To mitigate antitrust risks, the ocular telehealth program should identify aspects of the program that threaten competition and implement appropriate safeguards under the guidance of council.

E. State medical practice acts/licensure

In general, telehealth legal issues assume telemedicine is the practice of medicine, and telemedicine and telehealth programs are subject to the ordinary laws and regulatory oversight that govern all medical providers. These issues are addressed variably by state medical practice acts, but even in the absence of specific statutory or regulatory definitions, telehealth legal claims would be difficult to defend against otherwise.³³

All 50 states, the District of Columbia, and the U.S. territories require licensure for rendering medical care to patients located in their jurisdiction, and a physician is considered subject also to the medical practice laws and regulations where the patient is located. Many states provide for some degree of telemedicine-friendly licensure or license “portability” for telemedicine, including a small number of states with telemedicine or special purpose licensure, and a larger number with participation in the Interstate Medical Licensure Compact.⁸⁸ This compact allows qualified physicians seeking to practice in multiple states to be eligible for expedited licensure in all states participating in the compact.

The ATA Interstate Telehealth Special Interest Group (SIG) is a source of current information on cross-border practice developments.⁸⁹ Since this is an active topic for legislative attention in many states, all programs should closely examine the licensure options in states of intended practice.^90,91

F. Tort liability

Telemedicine may reduce overall liability risks through improved access and quality of care and improved documentation. However, experience indicates that telemedicine may increase the risk for liability for providers and facilities that use it and for those who chose to not use it. The elements of a medical malpractice claim are well established, but telemedicine can also complicate traditional tort liability. Issues include which entity or physician owes a duty to the patient, standards-of-care, jurisdiction, and choice of law.³³ Although telemedicine providers should consult an attorney familiar with telemedicine law, the fundamental aspects of tort law are fairly uniform across jurisdictions:

A physician has a duty to a patient to act within the accepted standards-of-care.

Standards-of-care were violated.

A patient suffered an injury due to the violation of standard-of-care.

Duty

A physician's duty arises from the physician–patient relationship.⁹² Telemedicine alters the traditional context of this relationship but a telemedicine encounter is sufficient to establish the relationship.^33,93

Standards-of-care

The American Medical Association believes medical specialty societies should develop or participate in the development and implementation of telemedicine clinical guidelines and position statements.⁹⁴ Because telemedicine standards-of-care are not universally established and recognized, questions could arise regarding appropriateness of a telemedicine DR evaluation, whether appropriate technology was selected (e.g., Validation Category 1, 2, 3 or 4), or whether the outcome was appropriate for a particular setting or case. An example of a controversial outcome is failure to diagnose nondiabetic retinopathy pathology evident in images (e.g., venous occlusion and choroidal neovascular membrane [CNVM]), or not evident in images (e.g., choroidal melanoma anterior to the equator and peripheral retinal tear/retinal detachment).

Issues of jurisdiction, choice of laws, and apportionment of liability are additional issues that are incompletely defined by statute and case law.⁹⁵ Telehealth providers should consult with legal counsel and their professional liability carrier to ensure proper risk management and medical liability coverage in both OS and DS.

G. Consent

Patients have the right to autonomous informed participation in health care decisions,⁹⁶ but this right cannot be exercised without enough information to allow an informed choice.⁹⁷ Informed consent is required for clinical treatments and procedures, including those delivered through telemedicine. When treatments or procedures delivered through ocular telehealth are considered low risk and within commonly accepted standards of practice, oral consent may be sufficient and a written and signed consent may not be required.⁸¹ Ocular telehealth services for DR may satisfy these criteria. Patients should be informed that they have a choice of telehealth and nontelehealth ocular assessment, treatments, or procedures. Practitioners should provide patients' information about the ocular telehealth program they would reasonably want to know, including:

Whether the services is novel or experimental.

Differences between care delivered using ocular telehealth and face-to-face examination.

Benefits and risks of using ocular telehealth in the patient's situation.

Description of what is to be done at the patient's site and the remote site.

Informed consent requirements vary from state to state, and currently, only a few states have laws that mandate informed consent for telemedicine treatment. However, ocular telehealth providers and programs should consult the statutes in their jurisdiction to determine whether oral or written informed consent is required for the telehealth services they render.

II. Quality control

A structured process for quality control and ongoing performance improvement is fundamental in health care,⁹⁸ and no less so for Tmed-DR. A codified method for collection, analysis, and reporting of programmatically relevant data must be used to document clinical and programmatic outcomes, and ensure patient safety, regulatory compliance, patient and provider satisfaction, and program sustainability. This process is necessarily end-to-end in scope, defined by specific and quantifiable quality measures relevant to the program, and must include a process for correction of identified fallout.

The technical quality of images and completeness of associated clinical data shall be assessed on an ongoing basis to ensure that their interpretation meets specified standards. Policies must be in place to ensure patient care and safety,^70,99,100 including addressing non-DR eye diseases and findings not specifically related to DM. Ocular telehealth programs shall also develop protocols that include policies and procedures for monitoring and evaluating performance.⁸¹

Corrective action of undesired trends and context-sensitive continuing education (CE) shall be included. Evaluation shall be tailored to include all components, such as image acquisition, transmission, reading and reporting, as well as related features such as reading latency, reporting duration, and referral completion. Image acquisition and reading quality assessment and performance improvement are similar to clinical settings. Quality assessment shall measure staff performance, data quality, and workflow. In the case of licensed providers, peer review of clinical outcome and identification of fallout cases to guide corrective interventions shall be performed in alignment with local policy and accreditation requirements.^101,102 Training and education standards shall be developed. An example of performance categories and measures, and training and QA methods is included in Appendix A6.

III. Operations

An operations manual is a comprehensive documentation of how a program functions on a daily and ongoing basis. A DR operations manual contains operational information and description of key processes in sufficient detail to provide standardized performance at all levels of the program, and also guide new leadership and staff. It can also describe QA and staff training procedures, but is not intended to function as an employee handbook. A comprehensive manual enables normal operations during leadership absence, and provides a pathway to programmatic sustainability during staffing changes at any level. Ocular telehealth programs should develop and implement an operations manual that is dynamic and evolves to remain aligned with program methods and goals.

IV. Customer support

Ocular telehealth programs use advanced technology in a broad range of settings, operated by diverse staff with varying training and expertise. Ocular telehealth programs should have a structured support system tailored to meet the needs of both internal and external customers. This support can be categorized by:

A. Originating site

Imager: imaging process, hardware/software, initial training and provisional certification, recurrent training, QA, and evidenced-based recertification.

Imaging device: image acquisition, operator-based service, device faults, preventive maintenance, site-based calibration, and diagnostics.

Provider/clinical contact: report retrieval/delivery and interpretation, patient recall, and billing.

B. Data transmission

Connectivity/network errors.

Data loss/recovery.

C. Distant site

Reader adjudication, initial training and provisional certification, QA/peer review, recurrent training, and evidence-based recertification.

Diagnostic display equipment and software.

OS and DS may be in the same facility with data transmission contained within a single local area network. Support for such systems is typically less complex than geographically distributed programs involving multiple networks and servers. Technical support can be divided into levels, or tiers, depending on difficulty or urgency. Tiered help desks are common and a convenient way to accommodate program needs efficiently. A DR telehealth program should establish standards for addressing customer support needs and tracking resolution of operational and technical problems. The outcome of customer support should be a routine component of the program's larger QA program. Appendix A7 provides examples of support levels and support priority.

V. Financial factors

Telehealth program sustainability depends on a well-developed and executed business plan. The actual cost of services can be a complex calculation, and reimbursement depends upon accurate diagnostic and procedural coding, and pays for performance and quality incentives. The specifics of these issues vary between regions, payers, and clinical settings, so each program should tailor billing protocols with Medicare, Medicaid, and private insurance intermediaries.

A. Reimbursement

Billing codes and reimbursement coverage are pivotal components for successful reimbursement. Billing is usually divided into technical or image capture (Current Procedural Terminology [CPT] suffix TC) and professional or interpretation components (CPT suffix 26). Before 2011, most DR telehealth programs used the 92250 (Fundus Photography with Interpretation and Report) CPT code. Infrequently, programs used CPT 92499 (Unlisted Ophthalmic Service or Procedure), which requires negotiated use with the fiscal intermediary or carrier.

In 2011, the CMS approved two new codes specific for remote retinal imaging, CPT 92227 and 92228. The reimbursement landscape is highly dynamic, and has substantial state, regional, and payer differences. Failure to attend to these changing differences appropriately can result in failed reimbursement and, in some instances, costly penalties. For these reasons, programs should seek ongoing council to ensure compliance with the requirements of a particular payer or fiscal intermediary, and locale. See Appendix A8 for additional information regarding billing and reimbursement of DR ocular telehealth services.

B. Grants

Grants have been used to establish telemedicine programs for defined circumstances and duration. Although an important method for proof of concept, grants are usually not viable for sustained clinical operation. As telehealth programs become more common as routine tools for health care, grants have become less common business plans for DR ocular telehealth. DR telehealth programs should have business plans that ensure revenue for sustainability, usually through reimbursement for services through Medicare, Medicaid, private insurance carriers, or per capita or transaction-based contracts.

C. Federal programs

There are several large telemedicine programs that reside within federal agencies and are funded by recurring federal appropriations. Examples include the Indian Health Service and the Veterans Health Administration. These programs sometime supplement their federal appropriations with external reimbursements, but their predominant business plan is cost avoidance through improved outcomes stemming from increased compliance with standards-of-care.

D. Other financial factors

Nonrevenue financial benefits of a DR telehealth program may include cost savings over traditional care delivery; however, benefits may not be realized by the entity creating them. For example, patients and third-party payers may realize financial savings through cost avoidance produced by a DR telehealth program, whereas the primary care physicians funding the program realize little or no direct savings.

Under current reimbursement policies in the United States, DR telehealth may be a better business model in closed systems, such as managed care, where costs and return on investment are realized by the same entity that funds and operates the program. However, it is important to recognize that cost-avoidance benefits occur over time and may not immediately offset day-to-day operational expenses. Government pay-for-performance incentive programs may change the relationship between program funding and reimbursement in the future. Appendix A8 contains financial information on logistic efficiencies, disease prevention, and resource utilization.

E. Equipment cost

Imaging costs depend on many factors, but with the decreasing cost of computing and telecommunications, a retinal camera is frequently the largest capital investment for a DR telehealth program. Retinal imaging devices range from $3,500 to >$85,000, including fundus camera, camera back, auxiliary lenses, computer, software, and network hardware. Almost all retinal imagers used by ocular telehealth DR programs are adaptations of devices designed for conventional eye clinic use. Consequently, they have technical and operation features and price points that are not optimized for the telehealth setting. The specific imaging device selected for a particular ocular telehealth DR program depends on its target clinical goals, business plan, clinical design, and other factors tied to clinical outcome and program scalability and sustainability. An ocular telehealth DR program should carefully weigh these factors before selecting a specific retinal imager.

Abbreviations

AEs

adverse events

artificial intelligence

AMD

age-related macular degeneration

AREDS

Age-Related Eye Disease Study

ATA

American Telemedicine Association

BIO

binocular indirect ophthalmoscopy

CAH

critical access hospital

CBIA

computer-based image analysis

continuing education

CMS

Centers for Medicare and Medicaid Services

CNNs

convolutional neural networks

CNV

choroidal neovascularization

CNVM

choroidal neovascular membrane

CoP

conditions of participation

CPT

Current Procedural Terminology

CQI

continuous quality improvement

CSME

clinically significant macular edema

DICOM

Digital Imaging and Communications in Medicine

diabetes mellitus

DME

diabetic macular edema

diabetic retinopathy

distant site

EHRs

electronic health records

EMR

electronic medical record

ETDRS

Early Treatment Diabetic Retinopathy Study

FDA

Food and Drug Administration

FOV

field of view

geographic atrophy

hard exudates

HIPAA

Health Insurance Portability and Accountability Act

HIT

health information technology

HL7

Health Level 7

IHE

Integrating the Healthcare Enterprise

LIP

licensed independent provider

MACs

Medicare Administrative Contractors

NICU

neonatal intensive care unit

NPDR

nonproliferative diabetic retinopathy

OCT

optical coherence tomography

originating site

PACS

Picture Archiving and Communication System

PDR

proliferative diabetic retinopathy

PHI

protected health information

PMS

patient management system

quality assurance

QALY

quality-adjusted life year

RFP

request for proposal

RNFL

retinal nerve fiber layer

ROC

receiver operating characteristics

ROP

retinopathy of prematurity

RVUs

relative value units

RW-ROP

referral-warranted ROP

SIG

Special Interest Group

SNOMED CT

Systematic Nomenclature of Medicine Clinical Terms

SNOMED

Systematized Nomenclature of Medicine

STDR

sight-threatening diabetic retinopathy

SUNDROP

Stanford University Network for Diagnosis of Retinopathy of Prematurity

TJC

The Joint Commission (formerly Joint Commission for the Accreditation of Healthcare Organizations)

Tmed-DR

telemedicine for DR

TRCs

Telehealth Resource Centers

VEGF

vascular endothelial growth factor

Glossary

DICOM: Digital Imaging and Communication in Medicine.

An international standard for distributing, storing, and viewing medical images.

HL7: Health Level 7.

An international framework for the electronic exchange of clinical, financial, and administrative information among computer systems in hospitals, clinical laboratories, pharmacies, etc.

IHE: Integrating the Healthcare Enterprise.

A global initiative by health care professionals and industry to improve computer sharing of health care information through coordinated use of established standards such as DICOM and HL7.

SNOMED CT^®: Systematic Nomenclature of Medicine Clinical Terms.

A system of clinical health care terminology covering diseases, findings, procedures, microorganisms, pharmaceuticals, etc.

Footnotes

Appendices

References

Hogenbirk

, Brockway

, Finley

, et al. Framework for Canadian telehealth guidelines: Summary of the environmental scan. J Telemed Telecare, 2006; 12:64–70.

TeleSCoPE Project. European code of practice for telehealth services. 2014. Available at www.telehealthcode.eu/images/stories/telehea/pdf/TELESCOPE_2014_CODE_FINAL_PDF_-_RELEASE_29_OCT_2013.pdf (last accessed December 29, 2019 ).

International Organization for Standardization. 2018. Available at www.iso.org (last accessed December 29, 2019 ).

American Telemedicine Association. Core operational guidelines for teleheatlh service involving provider-patient interactions. 2014. Available at thesource.americantelemed.org/resources/telemedicine-practice-guidelines (last accessed December 29, 2019 ).

Liu

, Torres Diaz

, Benkert

. Scaling up teleophthalmology for diabetic eye screening: Opportunities for widespread implementation in the USA. Curr Diab Rep, 2019; 19:74.

Grading diabetic retinopathy from stereoscopic color fundus photographs—An extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology , 1991; 98(5 Suppl.):786–806.

Gangaputra

, Almukhtar

, Glassman

, et al. Comparison of film and digital fundus photographs in eyes of individuals with diabetes mellitus. Invest Ophthalmol Vis Sci, 2011; 52:6168–6173.

, Danis

, Hubbard

, Florez-Arango

, Esquivel

, Krupinski

. Comparability of digital photography with the ETDRS film protocol for evaluation of diabetic retinopathy severity. Invest Ophthalmol Vis Sci, 2011; 52:4717–4725.

Scanlon

, Dirani

, Van Wijngaarden

. Screening for sight-threatening diabetic retinopathy: An update. Egypt Retina J, 2014; 2:3.

10.

Fransen

, Leonard-Martin

, Feuer

, Hildebrand

, Inoveon Health Research

Group

. Clinical evaluation of patients with diabetic retinopathy: Accuracy of the Inoveon diabetic retinopathy-3DT system. Ophthalmology, 2002; 109:595–601.

11.

Bursell

, Cavallerano

, et al. Stereo nonmydriatic digital-video color retinal imaging compared with Early Treatment Diabetic Retinopathy Study seven standard field 35-mm stereo color photos for determining level of diabetic retinopathy. Ophthalmology, 2001; 108:572–585.

12.

Rudnisky

, Hinz

, Tennant

, de Leon

, Greve

. High-resolution stereoscopic digital fundus photography versus contact lens biomicroscopy for the detection of clinically significant macular edema. Ophthalmology, 2002; 109:267–274.

13.

Cavallerano

, Cavallerano

, Katalinic

, et al. Use of Joslin Vision Network digital-video nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina, 2003; 23:215–223.

14.

Wilkinson

, Ferris

, 3rd, Klein

, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 2003; 110:1677–1682.

15.

The Diabetic Retinopathy Clinical Research Network. Protocol AA: Peripheral diabetic retinopathy (DR) lesions on ultrawide-field fundus images and risk of DR worsening over time protocol version 4.0. 2017. Available at https://public.jaeb.org/drcrnet (last accessed December 30, 2019 ).

16.

Scanlon

PH.

The English National Screening Programme for diabetic retinopathy 2003–2016. Acta Diabetol, 2017; 54:515–525.

17.

Landis

, Koch

. The measurement of observer agreement for categorical data. Biometrics, 1977; 33:159–174.

18.

Horton

, Silva

, Cavallerano

, Aiello

. Operational components of telemedicine programs for diabetic retinopathy. Curr Diab Rep, 2016; 16:128.

19.

Scanlon

, Foy

, Malhotra

, Aldington

. The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening. Diabetes Care, 2005; 28:2448–2453.

20.

Murgatroyd

, Ellingford

, Cox

, et al. Effect of mydriasis and different field strategies on digital image screening of diabetic eye disease. Br J Ophthalmol, 2004; 88:920–924.

21.

Horton

, Silva

, Cavallerano

, Aiello

. Clinical components of telemedicine programs for diabetic retinopathy. Curr Diab Rep, 2016; 16:129.

22.

Hreidarsson

AB.

Pupil size in insulin-dependent diabetes. Relationship to duration, metabolic control, and long-term manifestations. Diabetes , 1982; 31(5 Pt. 1):442–448.

23.

Klein

, Klein

, Wang

, Moss

. Older-onset diabetes and lens opacities. The Beaver Dam Eye Study. Ophthalmic Epidemiol , 1995; 2:49–55.

24.

Pandit

, Taylor

. Mydriasis and glaucoma: Exploding the myth. A systematic review. Diabet Med , 2000; 17:693–699.

25.

Liew

, Mitchell

, Wang

, Wong

. Fundoscopy: To dilate or not to dilate?. BMJ, 2006; 332:3.

26.

Whited

JD.

Accuracy and reliability of teleophthalmology for diagnosing diabetic retinopathy and macular edema: A review of the literature. Diabetes Technol Ther, 2006; 8:102–111.

27.

Bresnick

, Mukamel

, Dickinson

, Cole

. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Ophthalmology, 2000; 107:19–24.

28.

, Hubbard

, Danis

, Florez-Arango

, Esquivel

, Krupinski

. Comparison of multiple stereoscopic and monoscopic digital image formats to film for diabetic macular edema evaluation. Invest Ophthalmol Vis Sci, 2010; 51:6753–6761.

29.

Browning

, Altaweel

, Bressler

, Scott

, Diabetic Retinopathy Clinical Research

Network

. Diabetic macular edema: What is focal and what is diffuse?. Am J Ophthalmol, 2008; 146:649.e6–655.e6.

30.

Wang

, Tadarati

, Wolfson

, Bressler

. Comparison of prevalence of diabetic macular edema based on monocular fundus photography vs optical coherence tomography. JAMA Ophthalmol, 2016; 134:222–228.

31.

Litvin

, Bresnick

, Cuadros

, Selvin

, Kanai

, Ozawa

. A revised approach for the detection of sight-threatening diabetic macular edema. JAMA Ophthalmol, 2017; 135:62–68.

32.

Gulshan

, Peng

, Coram

, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016; 316:2402–2410.

33.

Blum

JD.

Internet medicine and the evolving legal status of the physician-patient relationship. J Leg Med, 2003; 24:413–455.

34.

Agha

, Schapira

, Laud

, McNutt

, Roter

. Patient satisfaction with physician-patient communication during telemedicine. Telemed J E Health, 2009; 15:830–839.

35.

Liu

, Zupan

, Swearingen

, et al. Identification of barriers, facilitators and system-based implementation strategies to increase teleophthalmology use for diabetic eye screening in a rural US primary care clinic: A qualitative study. BMJ Open, 2019; 9:e022594.

36.

Kasztelowicz

Security of medical data transfer and storage in internet cryptography, antiviral security and electronic signature problems, which must be solved in nearest future in practical contest. Pol J Pathol, 2003; 3:209–214.

37.

Brebner

, Brebner

, Ruddick-Bracken

, Wootton

, Ferguson

. The importance of setting and evaluating standards of telemedicine training. J Telemed Telecare, 2003; 9(Suppl. 1):S7–S9.

38.

Lee

Telemedicine: Opportunities and challenges for the remote care of diabetic retinopathy. Arch Ophthalmol, 1999; 117:1639–1640.

39.

Zeimer

, Zou

, Meeder

, Quinn

, Vitale

. A fundus camera dedicated to the screening of diabetic retinopathy in the primary-care physician's office. Invest Ophthalmol Vis Sci, 2002; 43:1581–1587.

40.

Silva

, Walia

, Cavallerano

, et al. Comparison of low-light nonmydriatic digital imaging with 35-mm ETDRS seven-standard field stereo color fundus photographs and clinical examination. Telemed J E Health, 2012; 18:492–499.

41.

Silva

, Cavallerano

, Sun

, Noble

, Aiello

. Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy. Am J Ophthalmol, 2012; 154:549.e2–559.e2.

42.

Mansberger

, Sheppler

, Barker

, et al. Long-term comparative effectiveness of telemedicine in providing diabetic retinopathy screening examinations: A randomized clinical trial. JAMA Ophthalmol, 2015; 133:518–525.

43.

DICOM^® Digital Imaging and Communications in Medicine. 2018. Available at https://www.dicomstandard.org (last accessed December 29, 2019 ).

44.

DICOM Standards Committee. Digital Imaging and Communications in Medicine (DICOM) supplement 91: Ophthalmic photography image SOP classes. 2004. Available at ftp://medical.nema.org/medical/dicom/final/sup91_ft2.pdf (last accessed December 29, 2019 ).

45.

DICOM Standards Committee. Digital Imaging and Communications in Medicine (DICOM) supplement 110: Ophthalmic tomography image storage SOP class. 2007. Available at ftp://medical.nema.org/medical/dicom/final/sup110_ft4.pdf (last accessed December 29, 2019 ).

46.

DICOM Standards Committee. Digital Imaging and Communications in Medicine (DICOM) supplement 173: Wide field ophthalmic photography image storage SOP classes. 2015. Available at ftp://medical.nema.org/medical/dicom/final/sup173_ft2.pdf (last accessed December 29, 2019 ).

47.

DICOM Standards Committee. Digital Imaging and Communications in Medicine (DICOM) supplement 197: Ophthalmic optical coherence tomography for angiographic imaging storage SOP classes. 2017. Available at ftp://medical.nema.org/medical/dicom/final/sup197_ft.pdf (last accessed December 29, 2019 ).

48.

The Association of Electrical Equipment and Medical Imaging Manufacturers. DICOM PS3.10 2013: Media storage and file format for media interchange. 7.1 DICOM file meta information. 2013. Available at http://dicom.nema.org/DICOM/2013/output/chtml/part10/chapter_7.html (last accessed December 29, 2019 ).

49.

Taylor

, Goatman

, Gregory

, et al. Image-quality standardization for diabetic retinopathy screening. Expert Rev Ophthalmol, 2009; 4:469–476.

50.

Toy

, Myung

, He

, et al. Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Retina, 2016; 36:1000–1008.

51.

Ryan

, Rajalakshmi

, Prathiba

, et al. Comparison among methods of retinopathy assessment (CAMRA) study: Smartphone, nonmydriatic, and mydriatic photography. Ophthalmology, 2015; 122:2038–2043.

52.

Yogesan

, Constable

, Barry

, Eikelboom

, McAllister

, Tay-Kearney

. Telemedicine screening of diabetic retinopathy using a hand-held fundus camera. Telemed J, 2000; 6:219–223.

53.

Stillman

, Gole

, Wootton

, et al. Telepaediatrics and diabetic retinopathy screening of young people with diabetes in Queensland. J Telemed Telecare, 2004; 10(Suppl. 1):92–94.

54.

Cuadros

, Bresnick

. Can commercially available handheld retinal cameras effectively screen diabetic retinopathy?. J Diabetes Sci Technol, 2017; 11:135–137.

55.

Stebbins

, Chaum

Patient follow up and outcomes after teleretinal imaging for diabetic retinopathy in a primary care network. IOVS 2018;59:(ARVO Abstract 5241).

56.

Healthcare Information and Management Systems Society. Definition of interoperability. 2013. Available at https://www.himss.org/sites/hde/files/d7/FileDownloads/HIMSS%20Interoperability%20Definition%20FINAL.pdf (last accessed December 29, 2019 ).

57.

Koppel

Patient safety and health information technology: Learning from our mistakes. Agency for healthcare research and quality. 2012. Available at https://psnet.ahrq.gov/perspective/patient-safety-and-health-information-technology-learning-our-mistakes (last accessed December 30, 2019 ).

58.

The Office of the National Coordinator for Health Information Technology. Connecting health and care for the nation: A shared national interoperability roadmap. Version 1.0. 2015. Available at https://www.healthit.gov/sites/default/files/hie-interoperability/nationwide-interoperability-roadmap-final-version-1.0.pdf (last accessed December 29, 2019 ).

59.

The Office of the National Coordinator for Health Information Technology. 2017 Interoperability standards advisory. 2017. Available at https://www.healthit.gov/sites/default/files/2017_isa_reference_edition-final.pdf (last accessed December 29, 2019 ).

60.

Baker

, Rudnisky

, Tennant

, et al. JPEG compression of stereoscopic digital images for the diagnosis of diabetic retinopathy via teleophthalmology. Can J Ophthalmol, 2004; 39:746–754.

61.

Rudnisky

, Tennant

, Weis

, Ting

, Hinz

, Greve

. Web-based grading of compressed stereoscopic digital photography versus standard slide film photography for the diagnosis of diabetic retinopathy. Ophthalmology, 2007; 114:1748–1754.

62.

DICOM Working Group 9. Digital Imaging and Communications in Medicine (DICOM) supplement 152 ophthalmic thickness map storage SOP class. 2011. Available at ftp://medical.nema.org/medical/dicom/final/sup152_ft.pdf (last accessed December 29, 2019 ).

63.

DICOM Working Group 9. Digital Imaging and Communications in Medicine (DICOM) supplement 143 DICOM SR template for reporting of macular grid thickness and volume. 2009. Available at ftp://medical.nema.org/medical/dicom/final/sup143_ft.pdf (last accessed December 29, 2019 ).

64.

Kuzmak

, Dayhoff

. Minimizing Digital Imaging and Communications in Medicine (DICOM) modality worklist patient/study selection errors. J Digit Imaging, 2001; 14(2 Suppl. 1):153–157.

65.

Csipo

, Dayhoff

, Kuzmak

. Integrating Digital Imaging and Communications in Medicine (DICOM)-structured reporting into the hospital environment. J Digit Imaging, 2001; 14(2 Suppl. 1):12–16.

66.

Integrating the Healthcare Enterprise. Technical framework. 2016. Available at https://www.ihe.net/Technical_Frameworks/#eyecare (last accessed December 29, 2019 ).

67.

American Academy of Ophthalmology. Preferred Practice Pattern^®. Diabetic retinopathy. 2017. Available at https://www.aao.org/preferred-practice-pattern/diabetic-retinopathy-ppp-updated-2017 (last accessed December 29, 2019 ).

68.

Evidence-Based Optometry Guideline Development Group. Eye care of the patient with diabetes mellitus, second Edition. St. Louis, MO: American Optometric Association. 2019.

69.

SNOMED International. Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT). 2018. Available at https://www.snomed.org (last accessed December 29, 2019 ).

70.

Silverman

RD.

Current legal and ethical concerns in telemedicine and e-medicine. J Telemed Telecare, 2003; 9(Suppl. 1):S67–S69.

71.

Bilimoria

NM.

Telemedicine: Laws still need a dose of efficiency. J Med Pract Manage, 2003; 18:289–294.

72.

HL7. Health Level Seven International (HL7). 2018. Available at www.hl7.org (last accessed December 29, 2019 ).

73.

The Joint Commission. 2018. Available at https://www.jointcommission.org (last accessed December 29, 2019 ).

74.

Accreditation Association for Ambulatory Health

Care

, Inc. 2018. Available at www.aaahc.org (last accessed December 29, 2019 ).

75.

The Joint Commission. Final revisions to telemedicine standards. 2012. Available at www.jointcommission.org/assets/1/6/Revisions_telemedicine_standards.pdf (last accessed December 29, 2019 ).

76.

National Association Medical Staff Services. NAMSS comparison of accreditation standards. 2017. Available at https://www.namss.org/Portals/0/Education/Instructor%20Resources/NAMSS%20Comparison%20of%20Accreditation%20Standards%202018.pdf (last accessed December 29, 2019 ).

77.

Department of Health and Human Services. Summary of the HIPAA privacy rule. 2017. Available at https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations (last accessed December 29, 2019 ).

78.

Department of Health and Human Services. The HIPAA privacy rule. 2017. Available at https://www.hhs.gov/hipaa/for-professionals/privacy/index.html (last accessed December 29, 2019 ).

79.

Department of Health and Human Services. The security rule. 2017. Available at https://www.hhs.gov/hipaa/for-professionals/security/index.html (last accessed December 29, 2019 ).

80.

Department of Health and Human Services. Summary of the HIPAA security rule. 2017. Available at https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations (last accessed December 29, 2019 ).

81.

Eliasson

, Poropatich

. Performance improvement in telemedicine: The essential elements. Mil Med, 1998; 163:530–535.

82.

Center for Connected Health Policy. Credentialing and privileging. 2018. Available at https://www.cchpca.org/telehealth-policy/credentialing-and-privileging (last accessed December 29, 2019 ).

83.

Criminal Penalties for acts involving Federal health care programs. 42 U.S.C. § 1320a-7b(b). 2010 (amended).

84.

Public Health Service Act; Program Integrity—Medicare and State Health Care Programs; Permissive Exclusions. 42 C.F.R. § 1001.952. 2016 (amended).

85.

Social Security Act; Limitation on certain physician referrals. 42 U.S.C. § 1395nn. 2010 (amended).

86.

Centers for Medicare and Medicaid Services. Physician self referral. 2015. Available at https://www.cms.gov/Medicare/Fraud-and-Abuse/PhysicianSelfReferral/index (last accessed December 29, 2019 ).

87.

Office of the Inspector General. Advisory Opinion No. 98-18. 1998. Available at https://oig.hhs.gov/fraud/docs/advisoryopinions/1998/ao98_18.htm (last accessed December 29, 2019 ).

88.

Interstate Medical Licensure Compact. 2018. Available at https://imlcc.org (last accessed December 29, 2019 ).

89.

American Telemedicine Association. ATA Interstate Telehealth Special Interest Group. 2018. Available at https://www.americantelemed.org/communities/interstate-health (last accessed December 29, 2019 ).

90.

American Telemedicine Association. State gap telemedicine gap analysis: Physician practice standards and licensure. 2018. Available at www.americantelemed.org/policy-page/state-telemedicine-gaps-reports (last accessed December 29, 2019 ).

91.

Center for Connected Health Policy. State telehealth laws and reimbursement policies. 2018. Available at https://www.cchpca.org/sites/default/files/2018-10/CCHP_50_State_Report_Fall_2018.pdf (last accessed December 29, 2019 ).

92.

Rigelhaupt

JL.

Annotation, what constitutes physician-patient relationship for malpractice purposes, 17 A.L.R. 4th 132. 1982.

93.

Fleisher

, Dechende

. E-health and antitrust. In: Telemedicine and e-health law, Rel 22. Ch1,1.04[3a]. New York: Law Journal Press, 2016.

94.

American Medical Association. Report 7 of the Council on Medical Service (A-14). 2014. Available at www.jonesday.com/files/upload/AMA%20Policy%20on%20Telehealth%20(June%202014).PDF (last accessed December 29, 2019 ).

95.

Fleisher

, Dechende

. Telemedicine medical malpractice liability. In: Telemedicine and e-health law. New York: Law Journal Press, 2016.

96.

Stanberry

Legal ethical and risk issues in telemedicine. Comput Methods Programs Biomed, 2001; 64:225–233.

97.

American Medical Association. The AMA code of medical ethics. Opinions on informing patients, opinion 8.08. Virtual Mentor , 2012; 14:555–556.

98.

Institute of Medicine (U.S.). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press, 2001.

99.

Iserson

KV.

Telemedicine: A proposal for an ethical code. Camb Q Healthc Ethics, 2000; 9:404–406.

100.

Mossman

KL.

Medical testing: Issues and ethics. Forum Appl Res Public Policy, 1997; 12:90–101.

101.

Borkowski

, Mielniczuk

. Telemedical management system of structured clinical documentation—application for quality assurance and multicenter clinical trials. Pol J Pathol, 2003; 54:193–195.

102.

McCrossin

Managing risk in telemedicine. J Telemed Telecare, 2003; 9(Suppl. 2):S36–S39.

103.

Cavallerano

, Lawrence

, Zimmer-Galler

, et al. Telehealth practice recommendations for diabetic retinopathy. Telemed J E Health, 2004; 10:469–482.

104.

Diabetic Retinopathy Study Group. A modification of the Airlie House classification of diabetic retinopathy, DRS report no. 7. Invest Ophthalmol Vis Sci , 1981; 21:210–226.

105.

Integrating the Healthcare Enterprise. Technical framework supplement. Unified Eye Care Workflow (U-EYECARE). 2015. Available at https://www.ihe.net/uploadedFiles/Documents/Eye_Care/IHE_EyeCare_Suppl_Unified_EC_Workflow_Rev1.1_TI_2015-07-14.pdf (last accessed December 29, 2019 ).

106.

The Office of the National Coordinator for Health Information Technology. What is the NHIN? 2010. Available at https://www.healthit.gov/sites/default/files/what-Is-the-nhin--2.pdf (last accessed December 29, 2019 ).

107.

Regenstrief

Institute

, Inc. LOINC (Logical Observation Identifiers Names and Codes). 2018. Available at https://loinc.org (last accessed December 29, 2019 ).

108.

Partnership on Artificial Intelligence. 2018. Available at https://www.partnershiponai.org (last accessed December 29, 2019 ).

109.

American Medical Association. Augmented intelligence in health care:. 2019 AI Board report. 2019. Available at https://www.ama-assn.org/system/files/2019-08/ai-2019-board-report.pdf (last accessed December 29, 2019 ).

110.

Helmchen

, Lehmann

, Abramoff

. Automated detection of retinal disease. Am J Manag Care, 2014; 20(11 Spec. No. 17):eSP48–eSP52.

111.

Food and Drug Administration. FDA permits marketing of artificial-intelligence-based device to detect certain diabetes-related eye problems. 2018. Available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm604357.htm (last accessed December 29, 2019 ).

112.

Food and Drug Administration. Software as a Medical Device (SaMD): Clinical evaluation. 2017. Available at https://www.fda.gov/media/100714/download (last accessed December 29, 2019 ).

113.

International Medical Device Regulators Forum SaMD Working Group. Software as a Medical Device (SaMD): Application of quality management system. 2015. Available at www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-151002-samd-qms.pdf (last accessed December 29, 2019 ).

114.

, Horton

, Bursell

, et al. Telehealth practice recommendations for diabetic retinopathy, second edition. Telemed J E Health, 2011; 17:814–837.

115.

Sim

, Keane

, Tufail

, Egan

, Aiello

, Silva

. Automated retinal image analysis for diabetic retinopathy in telemedicine. Curr Diab Rep, 2015; 15:14.

116.

Abramoff

, Garvin

, Sonka

. Retinal imaging and image analysis. IEEE Rev Biomed Eng, 2010; 3:169–208.

117.

Ryan

SJ.

Retina,, 6th ed. London, UK: Saunders/Elsevier, 2017.

118.

Abramoff

, Folk

, Han

, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol, 2013; 131:351–357.

119.

Fleming

, Goatman

, Philip

, Prescott

, Sharp

, Olson

. Automated grading for diabetic retinopathy: A large-scale audit using arbitration by clinical experts. Br J Ophthalmol. 2010; 94:1606–1610.

120.

Tufail

, Kapetanakis

, Salas-Vega

, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess, 2016; 20:1–72.

121.

Abramoff

, Lou

, Erginay

, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci, 2016; 57:5200–5206.

122.

Abramoff

, Fort

, Han

, Jayasundera

, Sohn

, Gardner

. Approach for a clinically useful comprehensive classification of vascular and neural aspects of diabetic retinal disease. Invest Ophthalmol Vis Sci, 2018; 59:519–527.

123.

Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology , 1991; 98(5 Suppl.):823–833.

124.

Haritoglou

, Kernt

, Neubauer

, et al. Microaneurysm formation rate as a predictive marker for progression to clinically significant macular edema in nonproliferative diabetic retinopathy. Retina, 2014; 34:157–164.

125.

US Food and Drug Administration. Facts about the current good manufacturing practices (CGMPs). 2018. Available at https://www.fda.gov/drugs/developmentapprovalprocess/manufacturing/ucm169105.htm (last accessed December 29, 2019 ).

126.

U.S. Food and Drug Administration. Part 820 quality system regulation. 2018. Available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=820 (last accessed December 29, 2019 ).

127.

International Organization for Standardization. Medical devices—Quality management systems—Requirements for regulatory purposes. 2016. Available at https://www.iso.org/standard/59752.html (last accessed December 29, 2019 ).

128.

Moons

, Stijnen

, Michel

, et al. Application of treatment thresholds to diagnostic-test evaluation: An alternative to the comparison of areas under receiver operating characteristic curves. Med Decis Making, 1997; 17:447–454.

129.

Cohen

, Korevaar

, Altman

, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: Explanation and elaboration. BMJ Open, 2016; 6:e012799.

130.

Shah

, Lynch

, Niemeijer

, et al. Susceptibility to misdiagnosis of adversarial images by deep learning based retinal image analysis algorithms. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, DC: IEEE,, 2018; 1454–1457.

131.

Lynch

, Abramoff

. Catastrophic failure in image-based convolutional neural network algorithms for detecting diabetic retinopathy. IOVS. 2017;58:(ARVO E-abstract 3776).

132.

Quellec

, Abramoff

. Estimating maximal measurable performance for automated decision systems from the characteristics of the reference standard. application to diabetic retinopathy screening. Conf Proc IEEE Eng Med Biol Soc , 2014; 2014:154–157.

133.

Abramoff

, Lavin

, Birch

, Shah

, Folk

. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med, 2018; 1:39.

134.

Mathieu

, Boutron

, Moher

, Altman

, Ravaud

. Comparison of registered and published primary outcomes in randomized controlled trials. JAMA, 2009; 302:977–984.

135.

DeAngelis

, Drazen

, Frizelle

, et al. Clinical trial registration: A statement from the International Committee of Medical Journal Editors. JAMA, 2004; 292:1363–1364.

136.

Ting

DSW

, Cheung

, Lim

, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA, 2017; 318:2211–2223.

137.

Scanlon

, Malhotra

, Greenwood

, et al. Comparison of two reference standards in validating two field mydriatic digital photography as a method of screening for diabetic retinopathy. Br J Ophthalmol, 2003; 87:1258–1263.

138.

Aldington

, Kohner

, Meuer

, Klein

, Sjolie

. Methodology for retinal photography and assessment of diabetic retinopathy: The EURODIAB IDDM complications study. Diabetologia, 1995; 38:437–444.

139.

Quellec

, Lamard

, Cazuguel

, et al. Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Invest Ophthalmol Vis Sci, 2011; 52:8342–8348.

140.

Abramoff

, Reinhardt

, Russell

, et al. Automated early detection of diabetic retinopathy. Ophthalmology, 2010; 117:1147–1154.

141.

Chaum

, Karnowski

, Govindasamy

, Abdelrahman

, Tobin

. Automated diagnosis of retinopathy by content-based image retrieval. Retina, 2008; 28:1463–1477.

142.

Fleming

, Goatman

, Philip

, et al. The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Br J Ophthalmol, 2010; 94:706–711.

143.

Abramoff

, Alward

, Greenlee

, et al. Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest Ophthalmol Vis Sci, 2007; 48:1665–1673.

144.

Ts'o

, Frostig

, Lieke

, Grinvald

. Functional organization of primate visual cortex revealed by high resolution optical imaging. Science, 1990; 249:417–420.

145.

Krizhevsky

, Sutskever

, Hinton

. Imagenet classification with deep convolutional neural networks. In: Pereira

, Burges

CJC

, Bottou

, Weinberger

, eds. Advances in neural information processing systems. New York: Curran Associates, Inc., 2012; 1097–1105.

146.

Kaggle. Diabetic retinopathy detection. 2015. Available at https://www.kaggle.com/c/diabetic-retinopathy-detection (last accessed December 29, 2019 ).

147.

Gargeya

, Leng

. Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 2017; 124:962–969.

148.

Takahashi

, Tampo

, Arai

, Inoue

, Kawashima

. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS One, 2017; 12:e0179790.

149.

Centers for Medicare and Medicaid Services. Covered entity guidance. 2016. Available at https://www.cms.gov/Regulations-and-Guidance/Administrative-Simplification/HIPAA-ACA/Downloads/CoveredEntitiesChart20160617.pdf (last accessed December 29, 2019 ).

150.

Social Security Act. 45 C.F.R. § 160.103. 2013 (amended).

151.

Social Security Act. 45 C.F.R. § 164.308(a)(7). 2013 (amended).

152.

Social Security Act. 45 C.F.R. § 164.308(a)(1). 2013 (amended).

153.

Federal Register. DHHS CMS: Telemedicine credentialing and privileging. 2011. Available at https://www.gpo.gov/fdsys/pkg/FR-2011-05-05/pdf/2011–10875.pdf (last accessed December 29, 2019 ).

154.

The Joint Commission. Telemedicine requirements. 2016. Available at https://www.gpo.gov/fdsys/pkg/FR-2011-05-05/pdf/2011–10875.pdf (last accessed December 29, 2019 ).

155.

Garvican

, Clowes

, Gillow

. Preservation of sight in diabetes: Developing a national risk reduction programme. Diabet Med, 2000; 17:627–634.

156.

Arun

, Young

, Batey

, et al. Establishing ongoing quality assurance in a retinal screening programme. Diabet Med, 2006; 23:629–634.

157.

Schneider

, Aldington

, Kohner

, et al. Quality assurance for diabetic retinopathy telescreening. Diabet Med, 2005; 22:794–802.

158.

American Medical Association. CPT (Current Procedural Terminology). 2017. Available at https://www.ama-assn.org/practice-management/cpt-current-procedural-terminology (last accessed December 29, 2019 ).

159.

Social Security Act. 42 USC 1395y§1862(a)(1)(A).

160.

Corcoran

SL.

CODING Q&A: Challenges in remote screening of diabetic patients, part 2. Retin Phys, 2019; 16:24, 26.

161.

Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology , 1991; 98(5 Suppl.):766–785.

162.

Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1. Early Treatment Diabetic Retinopathy Study research group. Arch Ophthalmol , 1985; 103:1796–1806.

163.

Javitt

, Aiello

, Chiang

, Ferris FL

I, Canner

, Greenfield

. Preventive eye care in people with diabetes is cost-saving to the federal government. Implications for health-care reform. Diabetes Care , 1994; 17:909–917.

164.

Javitt

, Aiello

. Cost-effectiveness of detecting and treating diabetic retinopathy. Ann Intern Med, 1996; 124(1 Pt 2):164–169.

165.

Whited

, Datta

, Aiello

, et al. A modeled economic analysis of a digital tele-ophthalmology system as used by three federal health care agencies for detecting proliferative diabetic retinopathy. Telemed J E Health, 2005; 11:641–651.

166.

Tham

, Li

, Wong

, Quigley

, Aung

, Cheng

. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology, 2014; 121:2081–2090.

167.

National Eye Institute. 2010 U.S. age-specific prevalence rates for glaucoma by age and race/ethnicity. 2010. Available at https://www.nei.nih.gov/learn-about-eye-health/resources-for-health-educators/eye-health-data-and-statistics/glaucoma-data-and-statistics (last accessed December 29, 2019 ).

168.

Rein

, Zhang

, Wirth

, et al. The economic burden of major adult visual disorders in the United States. Arch Ophthalmol, 2006; 124:1754–1760.

169.

Canadian Ophthalmological Society Glaucoma Clinical Practice Guideline Expert

Committee

, Canadian Ophthalmological

Society

. Canadian Ophthalmological Society evidence-based clinical practice guidelines for the management of glaucoma in the adult eye. Can J Ophthalmol, 2009; 44(Suppl. 1):S7–S93.

170.

Prum

Jr. , Rosenberg

, Gedde

, et al. Primary Open-Angle Glaucoma Preferred Practice Pattern((R)) guidelines. Ophthalmology, 2016; 123:P41–P111.

171.

Resnikoff

, Felch

, Gauthier

, Spivey

. The number of ophthalmologists in practice and training worldwide: A growing gap despite more than 200,000 practitioners. Br J Ophthalmol, 2012; 96:783–787.

172.

Kassam

, Yogesan

, Sogbesan

, Pasquale

, Damji

. Teleglaucoma: Improving access and efficiency for glaucoma care. Middle East Afr J Ophthalmol, 2013; 20:142–149.

173.

Blázquez

, Sebastián

, Antón

. [Detection of glaucoma using SisGlaTel: Acceptability and satisfaction among participants, and problems detected]. Arch Soc Esp Oftalmol, 2008; 83:533–538.

174.

Clarke

, Puertas

, Kotecha

, Foster

, Barton

. Virtual clinics in glaucoma care: Face-to-face versus remote decision-making. Br J Ophthalmol, 2017; 101:892–895.

175.

de Bont

, Bal

. Telemedicine in interdisciplinary work practices: On an IT system that met the criteria for success set out by its sponsors, yet failed to become part of every-day clinical routines. BMC Med Inform Decis Mak, 2008; 8:47.

176.

Hautala

, Hyytinen

, Saarela

, et al. A mobile eye unit for screening of diabetic retinopathy and follow-up of glaucoma in remote locations in northern Finland. Acta Ophthalmol, 2009; 87:912–913.

177.

Kassam

, Amin

, Sogbesan

, Damji

. The use of teleglaucoma at the University of Alberta. J Telemed Telecare, 2012; 18:367–373.

178.

Keenan

, Shahid

, Bourne

, White

, Martin

. Cambridge community optometry glaucoma scheme. Clin Exp Ophthalmol, 2015; 43:221–227.

179.

Kennedy

, Kirwan

, Cook

, Roux

, Stulting

, Murdoch

. Telemedicine techniques can be used to facilitate the conduct of multicentre trials. J Telemed Telecare, 2000; 6:343–347; discussion 347–349.

180.

Kiage

, Kherani

, Gichuhi

, Damji

, Nyenze

. The Muranga Teleophthalmology Study: Comparison of virtual (teleglaucoma) with in-person clinical assessment to diagnose glaucoma. Middle East Afr J Ophthalmol, 2013; 20:150–157.

181.

, Tang

, Oschner

, Koplos

, Grady

, Crump

. Telemedicine screening of glaucoma. Telemed J, 1999; 5:283–290.

182.

Owsley

, Rhodes

, McGwin

Jr. , et al. Eye Care Quality and Accessibility Improvement in the Community (EQUALITY) for adults at risk for glaucoma: Study rationale and design. Int J Equity Health, 2015; 14:135.

183.

Rhodes

, Huisingh

, McGwin

Jr. , et al. Eye Care Quality and Accessibility Improvement in the Community (EQUALITY): Impact of an eye health education program on patient knowledge about glaucoma and attitudes about eye care. Patient Relat Outcome Meas, 2016; 7:37–48.

184.

Staffieri

, Ruddle

, Kearns

, et al. Telemedicine model to prevent blindness from familial glaucoma. Clin Exp Ophthalmol, 2011; 39:760–765.

185.

Tuulonen

, Ohinmaa

, Alanko

, Hyytinen

, Juutinen

, Toppinen

. The application of teleophthalmology in examining patients with glaucoma: A pilot study. J Glaucoma, 1999; 8:367–373.

186.

Verma

, Arora

, Kassam

, Edwards

, Damji

. Northern Alberta remote teleglaucoma program: Clinical outcomes and patient disposition. Can J Ophthalmol, 2014; 49:135–140.

187.

Wright

, Diamond

. Service innovation in glaucoma management: Using a web-based electronic patient record to facilitate virtual specialist supervision of a shared care glaucoma programme. Br J Ophthalmol, 2015; 99:313–317.

188.

Kotecha

, Baldwin

, Brookes

, Foster

. Experiences with developing and implementing a virtual clinic for glaucoma care in an NHS setting. Clin Ophthalmol, 2015; 9:1915–1923.

189.

Thomas

, Hodge

, Malvankar-Mehta

. The cost-effectiveness analysis of teleglaucoma screening device. PLoS One, 2015; 10:e0137913.

190.

Court

, Austin

. Virtual glaucoma clinics: Patient acceptance and quality of patient education compared to standard clinics. Clin Ophthalmol, 2015; 9:745–749.

191.

Park

, Mansberger

. Eye disease in patients with diabetes screened with telemedicine. Telemed J E Health, 2017; 23:113–118.

192.

Cavallerano

, Cavallerano

, Katalinic

, et al. A telemedicine program for diabetic retinopathy in a Veterans Affairs Medical Center—the Joslin Vision Network Eye Health Care Model. Am J Ophthalmol, 2005; 139:597–604.

193.

Thomas

, Jeyaraman

, Hodge

, Hutnik

, Costella

, Malvankar-Mehta

. The effectiveness of teleglaucoma versus in-patient examination for glaucoma screening: A systematic review and meta-analysis. PLoS One, 2014; 9:e113779.

194.

Moyer

, U.S. Preventive Services Task Force. Screening for glaucoma: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med , 2013; 159:484–489.

195.

Bokman

, Pasquale

, Parrish

, 2nd, Lee

. Glaucoma screening in the Haitian Afro-Caribbean population of South Florida. PLoS One, 2014; 9:e115942.

196.

Waisbourd

, Pruzan

, Johnson

, et al. The Philadelphia Glaucoma Detection and Treatment Project: Detection rates and initial management. Ophthalmology, 2016; 123:1667–1674.

197.

Kassam

, Sogbesan

, Boucher

, et al. Collaborative care and teleglaucoma: A novel approach to delivering glaucoma services in Northern Alberta, Canada. Clin Exp Optom, 2013; 96:577–580.

198.

Kashiwagi

, Tanabe

, Go

, et al. Comparison of a remote operating slit-lamp microscope system with a conventional slit-lamp microscope system for examination of trabeculectomy eyes. J Glaucoma, 2013; 22:278–283.

199.

Liu

, Lam

, Weinreb

, et al. Comparison of standard automated perimetry, frequency-doubling technology perimetry, and short-wavelength automated perimetry for detection of glaucoma. Invest Ophthalmol Vis Sci, 2011; 52:7325–7331.

200.

Monsalve

, Ferreras

, Calvo

, et al. Diagnostic ability of Humphrey perimetry, Octopus perimetry, and optical coherence tomography for glaucomatous optic neuropathy. Eye (Lond), 2017; 31:443–451.

201.

Lowry

, Hou

, Hennein

, et al. Comparison of peristat online perimetry with the humphrey perimetry in a clinic-based setting. Transl Vis Sci Technol, 2016; 5:4.

202.

Vingrys

, Healey

, Liew

, et al. Validation of a tablet as a tangent perimeter. Transl Vis Sci Technol, 2016; 5:3.

203.

Tan

, Yu

, Baig

, Hansapinyo

, Tham

. Agreement of patient-measured intraocular pressure using rebound tonometry with Goldmann applanation tonometry (GAT) in glaucoma patients. Sci Rep, 2017; 7:42067.

204.

Chen

, Querat

, Akerstedt

. Self-tonometry as a complement in the investigation of glaucoma patients. Acta Ophthalmol, 2016; 94:788–792.

205.

Tojo

, Abe

, Ishida

, Yagou

, Hayashi

. The fluctuation of intraocular pressure measured by a contact lens sensor in normal-tension glaucoma patients and nonglaucoma subjects. J Glaucoma, 2017; 26:195–200.

206.

Tojo

, Hayashi

, Otsuka

, Miyakoshi

. Fluctuations of the intraocular pressure in pseudoexfoliation syndrome and normal eyes measured by a contact lens sensor. J Glaucoma, 2016; 25:e463–e468.

207.

Brandt

, Beiser

, Gordon

, Kass

, Ocular Hypertension Treatment Study

Group

. Central corneal thickness and measured IOP response to topical ocular hypotensive medication in the Ocular Hypertension Treatment Study. Am J Ophthalmol, 2004; 138:717–722.

208.

Barkana

, Gerber

, Elbaz

, et al. Central corneal thickness measurement with the Pentacam Scheimpflug system, optical low-coherence reflectometry pachymeter, and ultrasound pachymetry. J Cataract Refract Surg, 2005; 31:1729–1735.

209.

Tun

, Baskaran

, Tan

, Perera

, Aung

, Husain

. Evaluation of the anterior segment angle-to-angle scan of cirrus high-definition optical coherence tomography and comparison with gonioscopy and with the visante OCT. Invest Ophthalmol Vis Sci, 2017; 58:59–64.

210.

Han

, Cho

, Kang

. Correlation between optic nerve parameters obtained using 3D nonmydriatic retinal camera and optical coherence tomography: Interobserver agreement on the disc damage likelihood scale. J Ophthalmol, 2014; 2014:931738.

211.

Spaeth

, Henderer

, Liu

, et al. The disc damage likelihood scale: Reproducibility of a new method of estimating the amount of optic nerve damage caused by glaucoma. Trans Am Ophthalmol Soc, 2002; 100:181–185; discussion 185–186.

212.

Schrems

, Schrems-Hoesl

, Mardin

, Laemmer

, Kruse

, Horn

. Can glaucomatous visual field progression be predicted by structural and functional measures?. J Glaucoma, 2017; 26:373–382.

213.

, Fansi

, Boivin

, Joseph

, Harasymowycz

. Screening for glaucoma in high-risk populations using optical coherence tomography. Ophthalmology, 2010; 117:453–461.

214.

Springelkamp

, Lee

, Wolfs

, et al. Population-based evaluation of retinal nerve fiber layer, retinal ganglion cell layer, and inner plexiform layer as a diagnostic tool for glaucoma. Invest Ophthalmol Vis Sci, 2014; 55:8428–8438.

215.

Chong

, Lee

. Glaucoma versus red disease: Imaging and glaucoma diagnosis. Curr Opin Ophthalmol, 2012; 23:79–88.

216.

Haleem

, Han

, Hemert

, et al. Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (SLO) images. J Med Syst, 2016; 40:132.

217.

O'Day

, Smith

, Muir

, Turner

. Optometric use of a teleophthalmology service in rural Western Australia: Comparison of two prospective audits. Clin Exp Optom, 2016; 99:163–167.

218.

Quinn

, Ying

, Daniel

, et al. Validity of a telemedicine system for the evaluation of acute-phase retinopathy of prematurity. JAMA Ophthalmol, 2014; 132:1178–1184.

219.

Wang

, Callaway

, Wallenstein

, Henderson

, Leng

, Moshfeghi

. SUNDROP: Six years of screening for retinopathy of prematurity with telemedicine. Can J Ophthalmol, 2015; 50:101–106.

220.

Multicenter trial of cryotherapy for retinopathy of prematurity. 3 1/2-year outcome—structure and function. Cryotherapy for Retinopathy of Prematurity Cooperative Group. Arch Ophthalmol , 1993; 111:339–344.

221.

Good

, Early Treatment for Retinopathy of Prematurity Cooperative

Group

. Final results of the Early Treatment for Retinopathy of Prematurity (ETROP) randomized trial. Trans Am Ophthalmol Soc, 2004; 102:233–248; discussion 248–250.

222.

Gilbert

, Wormald

, Fielder

, et al. Potential for a paradigm change in the detection of retinopathy of prematurity requiring treatment. Arch Dis Child Fetal Neonatal Ed, 2016; 101:F6–F9.

223.

Bain

, Kristensen-Cabrera

, Lee

. A qualitative analysis of challenges and successes in retinopathy of prematurity screening. AJP Rep, 2018; 8:e128–e133.

224.

American Academy of Pediatrics Committee on Fetus and Newborn. Levels of neonatal care. Pediatrics, 2004; 114:1341–1347.

225.

Trese

MT.

What is the real gold standard for ROP screening?. Retina, 2008; 28(3 Suppl.):S1–S2.

226.

Ryan

, Ostmo

, Jonas

, et al. Development and evaluation of reference standards for image-based telemedicine diagnosis and clinical research studies in ophthalmology. AMIA Annu Symp Proc, 2014; 2014:1902–1910.

227.

Kovacs

, Somogyvari

, Maka

, Nagyjanosi

. Bedside ROP screening and telemedicine interpretation integrated to a neonatal transport system: Economic aspects and return on investment analysis. Early Hum Dev, 2017; 106–107:1–5.

228.

Quinn

, Ying

, Repka

, et al. Timely implementation of a retinopathy of prematurity telemedicine system. J AAPOS, 2016; 20:425.e1–430.e1.

229.

Fierson

, Capone

Jr. , American Academy of Pediatrics Section on

Ophthalmology

, American Academy of

Ophthalmology

, American Association of Certified

Orthoptists.

Telemedicine for evaluation of retinopathy of prematurity. Pediatrics, 2015; 135:e238–e254.

230.

Rathi

, Tsui

, Mehta

, Zahid

, Schuman

. The current state of teleophthalmology in the United States. Ophthalmology, 2017; 124:1729–1734.

231.

Campbell

, Kalpathy-Cramer

, Erdogmus

, et al. Plus disease in retinopathy of prematurity: A continuous spectrum of vascular abnormality as a basis of diagnostic variability. Ophthalmology, 2016; 123:2338–2344.

232.

Kalpathy-Cramer

, Campbell

, Erdogmus

, et al. Plus disease in retinopathy of prematurity: Improving diagnosis by ranking disease severity and using quantitative image analysis. Ophthalmology, 2016; 123:2345–2351.

233.

Campbell

, Ataer-Cansizoglu

, Bolon-Canedo

, et al. Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol, 2016; 134:651–657.

234.

Fleck

, Williams

, Juszczak

, et al. An international comparison of retinopathy of prematurity grading performance within the Benefits of Oxygen Saturation Targeting II trials. Eye (Lond), 2018; 32:74–80.

235.

Fierson

, American Academy of Pediatrics Section on

Ophthalmology

, American Academy of

Ophthalmology

, American Association for Pediatric Ophthalmology and

Strabismus

, American Association of Certified

Orthoptists

. Screening examination of premature infants for retinopathy of prematurity. Pediatrics, 2013; 131:189–195.

236.

Kemper

, Prosser

, Wade

, et al. A comparison of strategies for retinopathy of prematurity detection. Pediatrics, 2016; 137:1–10.

237.

Fierson

, American Academy of Pediatrics Section on

Ophthalmology

, American Academy of

Ophthalmology

, American Association for Pediatric Ophthalmology and

Strabismus

, American Association of Certified

Orthoptists

. Screening examination of premature infants for retinopathy of prematurity. Pediatrics, 2018; 142:e20183061.

238.

Vartanian

, Besirli

, Barks

, Andrews

, Musch

. Trends in the screening and treatment of retinopathy of prematurity. Pediatrics, 2017; 139:e20161978.

239.

Schwartz

, Harrison

, Ferrone

, Trese

. Telemedical evaluation and management of retinopathy of prematurity using a fiberoptic digital fundus camera. Ophthalmology, 2000; 107:25–28.

240.

Yen

, Hess

, Burke

, Johnson

, Feuer

, Flynn

. The optimum time to employ telephotoscreening to detect retinopathy of prematurity. Trans Am Ophthalmol Soc, 2000; 98:145–150; discussion 150–151.

241.

Chiang

, Wang

, Busuioc

, et al. Telemedical retinopathy of prematurity diagnosis: Accuracy, reliability, and image quality. Arch Ophthalmol, 2007; 125:1531–1538.

242.

Ells

, Holmes

, Astle

, et al. Telemedicine approach to screening for severe retinopathy of prematurity: A pilot study. Ophthalmology, 2003; 110:2113–2117.

243.

, Petersen

, VanderVeen

. RetCam imaging for retinopathy of prematurity screening. J AAPOS, 2006; 10:107–111.

244.

Chiang

, Keenan

, Starren

, et al. Accuracy and reliability of remote retinopathy of prematurity diagnosis. Arch Ophthalmol, 2006; 124:322–327.

245.

Photographic Screening for Retinopathy of Prematurity Cooperative Group. The photographic screening for retinopathy of prematurity study (photo-ROP). Primary outcomes. Retina , 2008; 28(3 Suppl.):S47–S54.

246.

Dhaliwal

, Wright

, Graham

, McIntosh

, Fleck

. Wide-field digital retinal imaging versus binocular indirect ophthalmoscopy for retinopathy of prematurity screening: A two-observer prospective, randomised comparison. Br J Ophthalmol, 2009; 93:355–359.

247.

Dai

, Chow

, Vincent

. Efficacy of wide-field digital retinal imaging for retinopathy of prematurity screening. Clin Exp Ophthalmol, 2011; 39:23–29.

248.

Kemper

, Wallace

, Quinn

. Systematic review of digital imaging screening strategies for retinopathy of prematurity. Pediatrics, 2008; 122:825–830.

249.

Quinn

, e-ROP Cooperative

Group

. Telemedicine approaches to evaluating acute-phase retinopathy of prematurity: Study design. Ophthalmic Epidemiol, 2014; 21:256–267.

250.

Cheng

, Daniel

, Pan

, et al. Plus disease in telemedicine approaches to evaluating acute-phase ROP (e-ROP) study: Characteristics, predictors, and accuracy of image grading. Ophthalmology, 2019; 126:868–875.

251.

Quinn

, Ells

, Capone

Jr.,

et al. Analysis of discrepancy between diagnostic clinical examination findings and corresponding evaluation of digital images in the telemedicine approaches to evaluating acute-phase retinopathy of prematurity study. JAMA Ophthalmol, 2016; 134:1263–1270.

252.

Biten

, Redd

, Moleta

, et al. Diagnostic accuracy of ophthalmoscopy vs telemedicine in examinations for retinopathy of prematurity. JAMA Ophthalmol, 2018; 136:498–504.

253.

Weaver

, Murdock

. Telemedicine detection of type 1 ROP in a distant neonatal intensive care unit. J AAPOS, 2012; 16:229–233.

254.

Vinekar

, Jayadev

, Mangalesh

, Shetty

, Vidyasagar

. Role of tele-medicine in retinopathy of prematurity screening in rural outreach centers in India: A report of 20,214 imaging sessions in the KIDROP program. Semin Fetal Neonatal Med, 2015; 20:335–345.

255.

Vinekar

, Gilbert

, Dogra

, et al. The KIDROP model of combining strategies for providing retinopathy of prematurity screening in underserved areas in India using wide-field imaging, tele-medicine, non-physician graders and smart phone reporting. Indian J Ophthalmol, 2014; 62:41–49.

256.

Ossandon

, Zanolli

, Stevenson

, Agurto

, Ortiz

, Dotan

. A national telemedicine network for retinopathy of prematurity screening. J AAPOS, 2018; 22:124–127.

257.

Lorenz

, Spasovska

, Elflein

, Schneider

. Wide-field digital imaging based telemedicine for screening for acute retinopathy of prematurity (ROP). Six-year results of a multicentre field study. Graefes Arch Clin Exp Ophthalmol , 2009; 247:1251–1262.

258.

Moitry

, Zarca

, Granier

, et al. Effectiveness and efficiency of tele-expertise for improving access to retinopathy screening among 351 neonates in a secondary care center: An observational, controlled before-after study. PLoS One, 2018; 13:e0206375.

259.

Chan

, Cougnard-Gregoire

, Korobelnik

, et al. Screening for retinopathy of prematurity by telemedicine in a tertiary level neonatal intensive care unit in France: Review of a six-year period. J Fr Ophtalmol, 2018; 41:926–932.

260.

Skalet

, Quinn

, Ying

, et al. Telemedicine screening for retinopathy of prematurity in developing countries using digital retinal images: A feasibility project. J AAPOS, 2008; 12:252–258.

261.

Karp

, Baumritter

, Pearson

, et al. Training retinal imagers for retinopathy of prematurity (ROP) screening. J AAPOS, 2016; 20:214–219.

262.

Vinekar

, Bhende

. Innovations in technology and service delivery to improve retinopathy of prematurity care. Community Eye Health, 2018; 31:S20–S22.

263.

Wood

, Moshfeghi

, Nudleman

, Moshfeghi

. Evaluation of Visunex Medical's PanoCam(TM) LT and PanoCam(TM) Pro wide-field imaging systems for the screening of ROP in newborn infants. Expert Rev Med Devices, 2016; 13:705–712.

264.

Phoenix Technology Group. 2019. Available at https://phoenixtech.com (last accessed December 29, 2019 ).

265.

Vinekar

, Dogra

, Chaitra

, Murthy

, Rao

, Shetty

. Evaluation of a new, low-cost, portable, wide-field, digital, retinal camera, “Neo” for screening infants for Retinopathy of Prematurity: A prospective, multi-center, validation report in Asian Indian infants. Invest Ophthalmol Vis Sci, 2016; 57:12.

266.

Prakalapakorn

, Wallace

, Freedman

. Retinal imaging in premature infants using the Pictor noncontact digital camera. J AAPOS, 2014; 18:321–326.

267.

Prakalapakorn

, Freedman

, Hutchinson

, et al. Evaluating a portable, noncontact fundus camera for retinopathy of prematurity screening by nonophthalmologist health care workers. Ophthalmol Retina, 2018; 2:864–871.

268.

Moshfeghi

, Capone

Jr . Economic barriers in retinopathy of prematurity management. Ophthalmol Retina, 2018; 2:1177–1178.

269.

Daniel

, Quinn

, Hildebrand

, et al. Validated system for centralized grading of retinopathy of prematurity: Telemedicine approaches to evaluating acute-phase retinopathy of prematurity (e-ROP) study. JAMA Ophthalmol, 2015; 133:675–682.

270.

Chan

, Patel

, Ryan

, et al. The Global Education Network for Retinopathy of Prematurity (Gen-Rop): Development, implementation, and evaluation of a novel tele-education system (An American Ophthalmological Society Thesis). Trans Am Ophthalmol Soc, 2015; 113:T2.

271.

Patel

, Martinez-Castellanos

, Berrones-Medina

, et al. Assessment of a tele-education system to enhance retinopathy of prematurity training by international ophthalmologists-in-training in Mexico. Ophthalmology, 2017; 124:953–961.

272.

Swanson

, Cocker

, Parker

, Moseley

, Fielder

. Semiautomated computer analysis of vessel growth in preterm infants without and with ROP. Br J Ophthalmol, 2003; 87:1474–1477.

273.

Heneghan

, Flynn

, O'Keefe

, Cahill

. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med Image Anal, 2002; 6:407–429.

274.

Ataer-Cansizoglu

, Bolon-Canedo

, Campbell

, et al. Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity: Performance of the “i-ROP” system and image features associated with expert diagnosis. Transl Vis Sci Technol, 2015; 4:5.

275.

Abbey

, Besirli

, Musch

, et al. Evaluation of screening for retinopathy of prematurity by ROPtool or a lay reader. Ophthalmology, 2016; 123:385–390.

276.

Redd

, Campbell

, Brown

, et al. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol, 2018; 103:580–584.

277.

Kapoor

, Walters

, Al-Aswad

. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol, 2019; 64:233–240.

278.

Brown

, Campbell

, Beers

, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol, 2018; 136:803–810.

279.

Wang

, Ju

, Chen

, et al. Automated retinopathy of prematurity screening using deep neural networks. EBioMedicine, 2018; 35:361–368.

280.

Zhang

, Wang

, Wu

, et al. Development of an automated screening system for retinopathy of prematurity using a deep neural network for wide-angle retinal images. IEEE Access, 2019; 7:10232–10241.

281.

Wade

, Pistilli

, Baumritter

, et al. Safety of retinopathy of prematurity examination and imaging in premature infants. J Pediatr, 2015; 167:994.e2–1000.e2.

282.

Prakalapakorn

, Stinnett

, Freedman

, Wallace

, Riggins

, Gallaher

. Non-contact retinal imaging compared to indirect ophthalmoscopy for retinopathy of prematurity screening: Infant safety profile. J Perinatol, 2018; 38:1266–1269.

283.

Jackson

, Scott

, Graff Zivin

, et al. Cost-utility analysis of telemedicine and ophthalmoscopy for retinopathy of prematurity management. Arch Ophthalmol, 2008; 126:493–499.

284.

Castillo-Riquelme

, Lord

, Moseley

, Fielder

, Haines

. Cost-effectiveness of digital photographic screening for retinopathy of prematurity in the United Kingdom. Int J Technol Assess Health Care, 2004; 20:201–213.

285.

Makkar

, McCoy

, Sekar

, Szyld

, Oestreich

, Siatkowski

. Utility of telemedicine for retinopathy of prematurity examination in level II NICU. J Okla State Med Assoc, 2018; 111:610–613.

286.

Vickers

, Freedman

, Wallace

, Prakalapakorn

. ROPtool analysis of images acquired using a noncontact handheld fundus camera (Pictor)—A pilot study. J AAPOS, 2015; 19:570–572.

287.

Munoz

, West

, Rubin

, et al. Causes of blindness and visual impairment in a population of older Americans: The Salisbury Eye Evaluation Study. Arch Ophthalmol, 2000; 118:819–825.

288.

Chou

, Dana

, Bougatsos

, Grusing

, Blazina

. Screening for impaired visual acuity in older adults: Updated evidence report and systematic review for the US Preventive Services Task Force. JAMA, 2016; 315:915–933.

289.

Chan

, Gangwani

, McGhee

, Lian

, Wong

. Cost-effectiveness of screening for intermediate age-related macular degeneration during diabetic retinopathy screening. Ophthalmology, 2015; 122:2278–2285.

290.

Maa

, Patel

, Chasan

, Delaune

, Lynch

. Retrospective evaluation of a teleretinal screening program in detecting multiple nondiabetic eye diseases. Telemed J E Health, 2017; 23:41–48.

291.

Tamura

, Goto

, Akune

, Hiratsuka

, Hiragi

, Yamada

. The clinical effectiveness and cost-effectiveness of screening for age-related macular degeneration in Japan: A Markov Modeling Study. PLoS One, 2015; 10:e0133628.

292.

Bressler

, Maguire

, Bressler

, Fine

. Relationship of drusen and abnormalities of the retinal pigment epithelium to the prognosis of neovascular macular degeneration. The Macular Photocoagulation Study Group. Arch Ophthalmol , 1990; 108:1442–1447.

293.

Scholl

, Dandekar

, Peto

, et al. What is lost by digitizing stereoscopic fundus color slides for macular grading in age-related maculopathy and degeneration?. Ophthalmology, 2004; 111:125–132.

294.

Pirbhai

, Sheidow

, Hooper

. Prospective evaluation of digital non-stereo color fundus photography as a screening tool in age-related macular degeneration. Am J Ophthalmol, 2005; 139:455–461.

295.

Duchin

, Asefzadeh

, Poulaki

, Rett

, Marescalchi

, Cavallerano

. Teleretinal imaging for detection of referable macular degeneration. Optom Vis Sci, 2015; 92:714–718.

296.

, Powell

, Hooper

, Sheidow

. Prospective evaluation of teleophthalmology in screening and recurrence monitoring of neovascular age-related macular degeneration: A randomized clinical trial. JAMA Ophthalmol, 2015; 133:276–282.

297.

De Bats

, Vannier Nitenberg

, Fantino

, Denis

, Kodjikian

. Age-related macular degeneration screening using a nonmydriatic digital color fundus camera and telemedicine. Ophthalmologica, 2014; 231:172–176.

298.

Ting

DSW

, Pasquale

, Peng

, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol, 2019; 103:167–175.

299.

Burlina

, Joshi

, Pekala

, Pacheco

, Freund

, Bressler

. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol, 2017; 135:1170–1176.

300.

Grassmann

, Mengelkamp

, Brandl

, et al. A deep learning algorithm for prediction of Age-Related Eye Disease Study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology, 2018; 125:1410–1420.

301.

Lee

, Baughman

, Lee

. Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. Ophthalmol Retina, 2017; 1:322–327.

302.

Kermany

, Goldbaum

, Cai

, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 2018; 172:1122.e9–1131.e9.

303.

Andonegui

, Aliseda

, Serrano

, et al. Evaluation of a telemedicine model to follow up patients with exudative age-related macular degeneration. Retina, 2016; 36:279–284.

304.

Azzolini

, Torreggiani

, Eandi

, et al. A teleconsultation network improves the efficacy of anti-VEGF therapy in retinal diseases. J Telemed Telecare, 2013; 19:437–442.

305.

AREDS2-Home Study Research Group, Chew

, Clemons

, et al. Randomized trial of a home monitoring system for early detection of choroidal neovascularization home monitoring of the eye (HOME) study. Ophthalmology, 2014; 121:535–544.

306.

Wittenborn

, Clemons

, Regillo

, Rayess

, Liffmann Kruger

, Rein

. Economic evaluation of a home-based age-related macular degeneration monitoring system. JAMA Ophthalmol, 2017; 135:452–459.

Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy,Third Edition

Abstract

Contributors

Table of Contents

Preamble

Scope

Introduction

Practice Guidelines

Clinical Guidelines

I. Principles of an ocular telehealth program for DR

A. Mission

B. Vision

C. Goals

D. Guiding principles

II. Ethics

III. Clinical validation

A. Category 1

B. Category 2

C. Category 3

D. Category 4

IV. Communication

V. Personnel qualifications

A. Medical care supervisor

B. Patient care coordinator

C. Image acquisition personnel

D. Image review and evaluation personnel

E. Information systems personnel

Technical Guidelines

I. Equipment specifications

A. Image acquisition

B. Image display

C. Image analysis

II. Data management

A. Interoperability

B. Compression

C. Data communication and transmission

D. Archiving and retrieval

E. Security

F. Reliability and redundancy

G. Documentation

Administrative Guidelines

I. Legal requirements

A. Facility accreditation

B. Health Insurance Portability and Accountability Act

C. Privileging and credentialing

D. Fraud and abuse

E. State medical practice acts/licensure

F. Tort liability

G. Consent

II. Quality control

III. Operations

IV. Customer support

A. Originating site

B. Data transmission

C. Distant site

V. Financial factors

A. Reimbursement

B. Grants

C. Federal programs

D. Other financial factors

E. Equipment cost

Abbreviations

Glossary

Footnotes

Appendices

References