Evaluation of medical device calibration knowledge,perceived importance,and implementation support among healthcare professionals in Ghana

Abstract

Objectives

Medical device calibration underpins accuracy, precision, and reliability, which are critical to optimal device performance and quality healthcare delivery. Despite its importance, little empirical evidence exists on calibration knowledge among healthcare professionals in sub-Saharan Africa. This study assessed such knowledge in Ghana by examining familiarity with calibration principles, procedures, device-specific practices, institutional support, and factors influencing competence.

Methods

A cross-sectional survey of 425 healthcare workers (technicians/clinical engineers, nurses, midwives, and doctors) employed a structured questionnaire. Statistical analyses (t-tests, ANOVA, and correlation analysis) were conducted to identify differences and associations in calibration knowledge and practices.

Results

Analyses revealed significant knowledge gaps. Technicians/clinical engineers outperformed other groups, while early-career professionals (1–3 years’ experience) scored higher than mid-career counterparts (7–10 years), highlighting shortcomings in continuous professional development. Formal training was also associated with higher scores.

Conclusion

These findings highlight the urgent need for structured, role-specific training and ongoing professional education to close calibration knowledge gaps, safeguard patient safety, and optimise device performance. The results provide evidence for policy reforms and workforce development strategies to strengthen medical device management and healthcare quality.

Graphical Abstract

1. Introduction

Medical devices have transformed modern healthcare.^1,2 These devices improve diagnosis and treatment, thereby improving patient outcomes. Effective device use can reduce hospital stays by up to 30%, lowering healthcare burdens. ³ Conversely, device failures compromise patient safety and care quality. Zamzam et al. reported that malfunctions in high-demand settings like ICUs disrupt service delivery and have led to serious injuries and fatalities. ⁴

Device performance hinges on accuracy, precision, and reliability,^2,5,6 which degrade over time due to environmental exposure and component wear.^7,8 Calibration, adjusting measurements to align with standards,^7,9,10 is essential to maintaining device performance. Calibration may be periodic or triggered by repairs, relocation, or suspected errors.^10–12 Failure to calibrate reduces device lifespan, increases maintenance costs, and risks patient harm. ² As such, calibration is mandated under various health quality and regulatory frameworks.^2,13,14 ISO 15189 explicitly includes calibration to ensure reliable test results. ¹⁵ Non-compliance with calibration standards can result in fines, legal action, reputational damage, or loss of licensure.^16–18

Despite its importance, calibration practices vary widely across healthcare systems, especially among healthcare workers without technical training. ¹⁹ Calibration of even simple devices, such as aneroid sphygmomanometers, is often neglected, leading to inaccurate blood pressure readings.^17,20 Zamzam et al. ⁴ identified limited calibration expertise and ineffective management strategies as common barriers. While Silbereisen et al.9 establish that systematic calibration integration into asset management systems (EAM) is a prerequisite for device safety and regulatory compliance, empirical evidence suggests a disconnect at the user level; Yayan and Zengin ⁸ found that although clinicians recognize this critical link to patient safety, they lack the practical training to implement the necessary calibration procedures, a finding also echoed by Alkanat. ²¹ While maintenance and calibration typically fall to technicians or clinical engineers, nurses and clinicians must also understand calibration requirements to avoid overlooked calibration tasks. In Ghana, as in other resource-limited settings, training on calibration procedures and schedules is essential to improve clinical competence and ensure measurement accuracy.^6,8,22 However, evidence on the calibration knowledge of Ghanaian healthcare professionals remains scarce, potentially undermining patient care quality and safety. Accordingly, this study assessed calibration knowledge, awareness, and institutional support among healthcare professionals in Ghana and analysed demographic and professional factors influencing calibration knowledge to inform targeted training and policy interventions.

2. Methods and materials

2.1. Study setting

Ghana’s healthcare system operates at three main levels: district hospitals (primary care), regional hospitals (secondary care), teaching hospitals (tertiary care), and the sole quaternary hospital. Public care is primarily managed by the Ghana Health Service and Ministry of Health, with private providers including faith-based and for-profit facilities. This multi-regional, multi-tier sampling ensured geographic diversity and national representativeness. This cross-sectional study was conducted between December 2024 and February 2025 across healthcare institutions in all sixteen administrative regions of Ghana.

2.2. Study population and selection criteria

The target population comprised healthcare professionals in Ghana who routinely use medical devices in clinical or diagnostic roles, including clinical engineers/technicians, doctors, nurses, and midwives. Eligible participants were actively employed in patient care and routinely used medical devices in their clinical or diagnostic duties and provided written informed consent. For this study, device handling refers to the day-to-day operation and use of medical devices on patients, which is integral to the roles of all included professional groups. Participants were excluded if they were: not actively employed in a clinical role at the time of data collection; engaged solely in administrative duties without patient care or device handling responsibilities; enrolled as students or trainees under supervised practice; or unwilling to provide informed consent.

2.3. Sample size calculation

The minimum sample size was calculated using Cochran’s formula at a 95% confidence level, 5% margin of error, and an assumed 50% response rate to maximise variability, yielding a minimum sample size of 384 participants. A total of 425 fully completed questionnaires were analysed, exceeding the minimum requirement and ensuring adequate statistical power for subgroup analyses.

2.4. Sampling technique

A multi-stage purposive sampling strategy was employed to capture geographical and institutional diversity. Surveys were administered online and on paper to improve reach, though this may have introduced response bias toward those with higher calibration awareness. Self-reported data also risked social desirability bias.

2.5. Data collection tool

Data were collected via a 29-item questionnaire covering demographics and calibration-related knowledge and practices. Items, derived from literature and best-practice guidelines,^2,8,11,22 were organised into six sections; Sections 2–6 used a 5-point Likert scale ²³ (1 = Strongly Disagree to 5 = Strongly Agree), a widely validated psychometric measurement tool. The questionnaire items include: sociodemographic information (10 items), awareness of calibration’s importance (5 items), knowledge of calibration procedures (4 items), device-specific calibration knowledge (3 items), impact on patient care (3 items), and training and support (3 items).

To ensure content validity and reliability, the questionnaire was validated through a two-stage process. First, it was piloted among 20 healthcare professionals with characteristics similar to those of the target population, whose feedback informed refinements to improve clarity, contextual relevance, and item appropriateness. Second, internal consistency was assessed using Cronbach’s alpha, which yielded a coefficient of 0.81, indicating high reliability. The fully validated questionnaire is provided as Supplementary File 1.

2.6. Data collection process

Questionnaires were administered over eight weeks (December 10, 2024 – February 2, 2025) via online and in-person methods. The online survey, hosted on Google Forms, supported self-paced completion with weekly reminders to improve participation, while printed copies were distributed on-site at healthcare facilities.

2.7. Data analysis

The analysis of the data collected involved several steps:

2.7.1. Data cleaning and preparation

Incomplete responses were excluded, yielding a complete dataset with no missing values. Data were checked for inconsistencies and outliers to ensure quality. Likert-scale responses and categorical demographics (e.g., training status, years of experience) were encoded for analysis.

2.7.2. Descriptive statistical analysis

Descriptive statistics were used to summarise participants’ sociodemographic profiles and questionnaire responses. Knowledge levels were assessed by calculating the maximum attainable scores and applying a 70% cutoff to distinguish between adequate and inadequate knowledge. This criterion was applied to each thematic section, enabling detailed distribution analysis. Proportions of respondents in each category were reported, alongside section-specific means and standard deviations. A histogram was used to illustrate overall response patterns.

2.7.3. Correlation analysis between sections

A correlation matrix was computed to examine associations between mean scores across questionnaire sections (e.g., Awareness of Calibration Importance and Knowledge of Calibration Procedures). Coefficients near +1 or −1 indicated strong positive or negative relationships, suggesting targeted improvements in one area may enhance others.

2.7.4. Comparative analysis

Analysis of variance (ANOVA) with post-hoc tests was used to assess differences in responses across demographic groups and questionnaire sections. Grouping variables included occupation, years of experience, and calibration training status. Mean scores, ANOVA results, and post-hoc comparisons identified statistically significant differences, highlighting groups and knowledge domains with the highest or lowest calibration competence. These findings inform targeted training initiatives and institutional policy improvements.

2.7.5. Power analysis

A post hoc power analysis using G*Power 3.1 evaluated the adequacy of the sample for detecting medium effect sizes (Cohen’s d = 0.5) at α = 0.05 and power = 0.80. For t-tests (trained vs. untrained) and one-way ANOVA (e.g., occupation, experience), the 425-participant sample achieved power > 0.95, confirming sufficient sensitivity to detect significant differences in calibration knowledge across groups.

2.8. Ethical considerations

Participants’ responses were anonymized, and confidentiality was maintained throughout the study. Ethical approval was obtained before the commencement of the study. All participants were fully informed about the purpose of the study, its voluntary nature, and their right to withdraw at any time without consequence. For in-person participants, written informed consent was obtained before completing the printed questionnaire. For online participants, a detailed consent statement was displayed at the beginning of the Google Form, and proceeding with the survey was accepted as confirmation of informed consent. To protect participant privacy, no personal identifiers were collected, and all responses were anonymised. Confidentiality was maintained throughout all stages of data collection, analysis, and reporting, and data were stored securely and accessed only by the research team.

3. Results

3.1. Participant characteristics

A total of 425 healthcare workers participated, 56.9% male, with ages ranging from 20 to over 50 years (mean = 32.1 ± 7.0 years); 83.8% were under 40 years. Mean professional experience was 3.92 ± 2.28 years, with 40% reporting 1–3 years. Nurses formed the largest group (42.5%), followed by technicians/clinical engineers, midwives, and doctors (Figure 1). Most respondents held a bachelor’s degree (58.5%), followed by diploma (18.8%), postgraduate (13.4%), and certificate-level (9.2%) qualifications. Nearly half (45.1%) used 3–4 medical devices daily, including a blood pressure monitor, pulse oximeter, glucometer, and thermometer; 32.4% worked in outpatient departments (Figure 1), and 58.1% had received calibration training. Most respondents were from the Ashanti region (20.9%; Supplementary Figure 1). Most respondents worked in public facilities (82.6%), particularly secondary-level hospitals (39.7%). Details on the sociodemographic characteristics of the respondents are available in Supplementary Table 1.

Figure 1.

Distribution of participants by (a) age group, (b) professional roles, and (c) work departments.

3.2. Distribution of responses by section

Figure 2 depicts Likert-scale response patterns across five sections. Understanding of Calibration Importance was overwhelmingly positive, with most selecting “Agree” or “Strongly Agree.” Knowledge of calibration procedures and device-specific knowledge showed broader distributions, though both remained skewed towards agreement. Perceived impact on patient care had the highest agreement levels, while training and support exhibited the widest spread, with substantial frequencies in “Neutral,” “Agree,” and “Strongly Agree”.

Figure 2.

Distribution of responses across five Likert-scale questions (a)–(e), illustrating the frequency of each response category from “Strongly Disagree” to “Strongly Agree.” Smooth density curves overlay the histograms to highlight response trends. Each panel represents a distinct section.

3.3. Mean scores and knowledge distribution across survey sections

The questionnaire consisted of 18 questions, with a total maximum score of 90 (Supplementary Table 2). A cutoff score of 63, representing 70% of the maximum score, was used to classify knowledge levels. Overall, 80.7% of respondents exceeded this threshold. Section-specific results showed the highest performance in: Understanding the Importance of Calibration section (max = 25; 89.9% above threshold) and Impact on Patient Care section (max = 15; 81.9% above threshold), with lower proportions in Knowledge of Calibration Procedures section (max = 20; 64.2%), Device-Specific Calibration Knowledge section (max = 15; 70.1%), and Training and Support section (max = 15; 63.5%).

Section score analysis showed clear variation across the five domains (Figure 3). Mean scores were highest for Understanding of Importance of Calibration (4.24 ± 0.79) and Impact on Patient Care (4.07 ± 0.87), and lowest for Device-Specific Knowledge (3.72 ± 1.07), Calibration Procedures (3.83 ± 1.02), and Training and Support (3.73 ± 1.05) (Supplementary Table 3).

Figure 3.

Mean Likert-scale scores across the five questionnaire sections (n = 425). Error bars represent ±1 standard deviation. Scores range from 1 (Strongly Disagree) to 5 (Strongly Agree). The y-axis begins at 3.0 to better illustrate differences between sections.

3.4. Correlation analysis between sections

Correlations among the five sections ranged from r = 0.44 to r = 0.80 (Figure 4). The strongest association was between Knowledge of Calibration Procedures and Device-Specific Calibration Knowledge (r = 0.80), followed by Device-Specific Calibration Knowledge and Training and Support (r = 0.73), and Knowledge of Calibration Procedures with Training and Support (r = 0.72). Impact on Patient Care moderately correlated with all other sections (r = 0.51–0.56). The weakest relationship was found between Understanding the Importance of Calibration and Training and Support (r = 0.44).

Figure 4.

Correlation heatmap showing the relationships between variables related to different calibration knowledge and practice dimensions, as represented by the various thematic sections. Correlation coefficients are represented by colour intensity, with values ranging from 0 (no correlation) to 1 (perfect correlation).

3.5. Analysis of variance and post-hoc comparisons across calibration knowledge domains

One-way ANOVA revealed significant differences in mean scores across all five sections (p < 0.001) (Supplementary Table 4). The Training and Support section showed the greatest variability (F = 84.74, p = 2.83e-35), followed by the Impact on Patient Care section (F = 44.51, p = 2.07e-19). The Knowledge of Calibration Procedures and Device-Specific Calibration Knowledge sections yielded F-values of 12.71 (p = 3.25e-08) and 7.00 (p = 0.000943), respectively.

Tukey’s HSD revealed the largest gaps between Device-Specific Calibration Knowledge and Understanding the Importance of Calibration (mean difference, Δ = 0.5194, p < 0.001) and between Training and Support and Understanding the Importance of Calibration (Δ = 0.5076, p < 0.001), both demonstrating strong statistical significance.

Other notable differences included Device-Specific Knowledge vs. Impact on Patient Care (Δ = 0.3514, p < 0.001) and Knowledge of Calibration Procedures vs. Impact on Patient Care (Δ = −0.2353, p < 0.001). Furthermore, significant differences were observed between Training and Support and Impact on Patient Care (Δ = -0.3396, p < 0.001), and between Knowledge of Calibration Procedures and Understanding the Importance of Calibration (Δ = 0.4033, p < 0.001).

3.6. Average mean scores by occupation

Occupational analysis revealed marked differences across the five domains (Supplementary Tables 5–6). Technicians/clinical engineers recorded the highest means in all sections, notably in Understanding the Importance of Calibration (4.51 ± 0.46), Knowledge of Procedures (3.98 ± 0.74), and Device-Specific Knowledge (3.91 ± 0.89), with similarly strong scores in Impact on Patient Care (4.15 ± 0.69) and Training and Support (3.81 ± 0.84). Doctors ranked second, excelling in Knowledge of Procedures (3.94 ± 0.76) and Impact on Patient Care (4.12 ± 0.57). Midwives performed moderately, peaking in Impact on Patient Care (4.15 ± 0.70). Nurses consistently had the lowest scores, particularly in Device-Specific Knowledge (3.61 ± 0.89), Training and Support (3.65 ± 0.81), and Understanding the Importance of Calibration (4.05 ± 0.60).

3.7. ANOVA and post-hoc analysis by occupation

One-way ANOVA (Supplementary Table 7) found significant differences in Understanding the Importance of Calibration (F = 16.89, p < 0.001) and Knowledge of Calibration Procedures (F = 3.42, p = 0.017), but not in Device-Specific Knowledge (F = 2.51, p = 0.058), Impact on Patient Care (F = 2.60, p = 0.098), or Training and Support (F = 2.24, p = 0.357). Post-hoc analysis showed nurses scored lower than doctors (Δ = –0.2408, p = 0.0027) in Understanding the Importance of Calibration, while technicians outperformed doctors (Δ = 0.2220, p = 0.0192), nurses (Δ = 0.4629, p < 0.0001), and midwives (Δ = 0.3353, p = 0.0057). No differences were seen between doctors and midwives (p = 0.6899) or midwives and nurses (p = 0.5444). For Knowledge of Calibration Procedures, only the nurse–technician gap was significant (Δ = 0.2637, p = 0.0327).

3.8. Average mean scores by experience

Experience-based analysis (Supplementary Tables 8–9) revealed distinct patterns. For the Understanding the Importance of Calibration section, respondents with ≥10 years’ experience scored highest (4.359 ± 0.54). In contrast, the Knowledge of Calibration Procedures section peaked among those with 1–3 years (3.907 ± 0.72) and was lowest among those with 7–10 years (3.438 ± 1.08). A similar pattern emerged in the Device-Specific Calibration Knowledge section, with 1–3 years scoring highest (3.831 ± 0.84) and 7–10 years lowest (3.325 ± 1.12).

For the Impact on Patient Care section, respondents <1 year (4.080 ± 0.68), 1–3 years (4.078 ± 0.61), and ≥10 years (4.181 ± 0.66) scored higher than the 7–10 years group (3.970 ± 0.66). In the Training and Support section, the lowest mean was again in the 7–10 years group (3.325 ± 1.10), while <1 year (3.796 ± 0.73) and 1–3 years (3.823 ± 0.78) recorded higher values.

3.9. ANOVA and post-hoc analysis scores by experience

As shown in Supplementary Table 10, ANOVA results indicated significant differences in three domains: Knowledge of Calibration Procedures (F = 3.35, p = 0.010), Device-Specific Calibration Knowledge (F = 3.19, p = 0.013), and Training and Support (F = 3.43, p = 0.009). No significant variation was observed for Understanding the Importance of Calibration (F = 0.63, p = 0.643) or Impact on Patient Care (F = 0.66, p = 0.617).

Post-hoc comparisons revealed consistently lower scores among respondents with 7–10 years’ experience in Knowledge of Calibration Procedures compared with those with 1–3 years Δ = −0.4685, p = 0.0041) and 4–6 years (Δ = −0.4540, p = 0.0108). The same group also scored lower in Device-Specific Calibration Knowledge than the 1–3 years (Δ = −0.5054, p = 0.0106) and 4–6 years (Δ = −0.4687, p = 0.0348) groups. In the Training and Support section, they scored lower than both 1–3 years (Δ = −0.4976, p = 0.0031) and 4–6 years (Δ = −0.4122, p = 0.0377), but higher than respondents <1 year (Δ = 0.4704, p = 0.0384).

3.10. Average mean scores by calibration training status

Across all five domains, trained respondents scored higher than untrained counterparts (Supplementary Tables 11–12). In the Understanding the Importance of Calibration section, trained participants averaged 4.416 ± 0.44 versus 3.986 ± 0.64 for untrained respondents. Similar patterns were observed for Knowledge of Calibration Procedures (4.146 ± 0.56 vs. 3.397 ± 0.87), Device-Specific Calibration Knowledge (4.130 ± 0.56 vs. 3.142 ± 1.04), Impact on Patient Care (4.195 ± 0.57 vs. 3.891 ± 0.70), and Training and Support (4.036 ± 0.62 vs. 3.301 ± 0.89).

3.11. T-test results by calibration training status

Independent t-tests confirmed significant differences between trained and untrained respondents in all sections (p < 0.001; Supplementary Table 13). The largest differences were for Device-Specific Calibration Knowledge (t = 11.525, p = 5.794e−25) and Knowledge of Calibration Procedures (t = 10.049, p = 1.652e−20). Significant differences also emerged for Understanding the Importance of Calibration (t = 7.742, p = 1.566e−13), Impact on Patient Care (t = 4.773, p = 2.722e−06), and Training and Support (t = 9.435, p = 1.192e−18), confirming that higher scores among trained participants were unlikely due to chance.

4. Discussion

Participants broadly recognised the importance of calibration, as evidenced by high mean scores in Understanding the Importance of Calibration (4.24 ± 0.79) and Impact on Patient Care (4.07 ± 0.87). However, notably lower scores in Knowledge of Calibration Procedures (3.83 ± 1.02) and Device-Specific Calibration Knowledge (3.72 ± 1.07), alongside the widest variability in Training and Support (3.73 ± 1.05), point to a clear disconnect between conceptual awareness and practical competence.

ANOVA revealed significant differences across all domains (p < 0.001), with Training and Support showing the greatest variability (F = 84.74). Post-hoc analysis revealed that Device-Specific Calibration Knowledge and Training and Support scored significantly lower than Understanding the Importance of Calibration, indicating a gap between conceptual awareness and applied skills. Strong correlations between procedural and device-specific knowledge (r = 0.80) and with training (r = 0.72 and 0.73, respectively) confirm training’s pivotal role. In contrast, the weakest link between awareness and institutional support (r = 0.44) underscores implementation gaps.

The predominantly early-career demographic profile of respondents (83.8% under 40; 66.4% with fewer than 10 years’ experience) is consistent with Ghana’s ongoing healthcare workforce expansion, which has seen a significant influx of newly trained professionals across all professional groups in recent years ²⁴ and emphasises the need for integration of calibration training into pre-service curricula. Nurses and midwives, whose dominance in the sample reflects typical healthcare staffing in low- and middle-income countries, ²⁵ recorded the lowest scores, a finding consistent with Alkanat et al. ²¹ and pointing to systemic gaps in calibration-focused curricula for non-technical staff. Addressing this requires deliberate integration of device calibration training into nursing and midwifery education programmes, rather than relying solely on on-the-job experience.

Experience-based analysis showed that early-career staff outperformed mid-career professionals in procedural and device-specific domains, suggesting benefits from recent curricular updates and a lack of continuous training for mid-career staff. Senior professionals (≥10 years) retained strong conceptual knowledge but demonstrated weaker technical competence.

Formal calibration training was the single strongest predictor of competency across all five domains. This finding aligns with international standards such as ISO 13485, which mandates that personnel must be qualified and appropriately trained to implement quality and safety protocols, including calibration processes. ²⁶ Comparative studies from Turkey and elsewhere report similar systemic gaps: high device use but low formal training rates.^8,21 Despite Ghana’s relatively lower proportion of untrained respondents (41.9%), the persistent skill gap confirms that device familiarity alone does not ensure calibration competence. Occupational disparities observed here mirror those reported, ²⁷ reinforcing the need for tailored, role-specific interventions.

The results reveal a workforce that values calibration but lacks consistent practical capability. Bridging this gap requires sustained, device-specific training, institutional support, and continuous professional development, particularly in resource-limited settings. Study limitations include potential selection bias due to purposive sampling, underrepresentation of remote/private facilities, and reliance on self-reported data, which may be influenced by social desirability bias. Broader, stratified sampling and longitudinal designs are recommended for future research.

5. Conclusion

This study identified substantial disparities in medical device calibration knowledge, awareness, and practices among healthcare professionals in Ghana. Although most participants recognised the importance of calibration, procedural and device-specific knowledge, training, and institutional support remained insufficient. Technicians and clinical engineers scored highest, consistent with their specialised responsibilities, while nurses and midwives scored lower, highlighting the need for role-specific interventions. Early-career professionals generally outperformed mid-career counterparts, emphasising the value of continuous professional development. Calibration training emerged as a strong determinant of higher competency, with trained respondents consistently achieving superior scores.

Bridging these gaps calls for a coordinated response across hospitals, national policy, and health education. Healthcare facilities should implement structured, role-specific training programmes with practical, hands-on components relevant to each clinical setting. At the same time, calibration competency should be formally embedded within Ghana’s national healthcare quality frameworks, including the Health Sector Medium-Term Development Plan, to ensure consistent standards across all facility levels. Equally, nursing, midwifery, and allied health training institutions must reform their curricula to include dedicated modules on medical device calibration, ensuring incoming professionals enter the workforce with the foundational knowledge needed to maintain device accuracy and patient safety. For practising professionals, periodic in-service refresher training should be institutionalised as a requirement rather than left to individual initiative. Together, these efforts are essential for closing the calibration knowledge gap, safeguarding patients, and raising the standard of healthcare delivery across Ghana and comparable resource-limited settings.

Supplemental material

Supplemental material - Evaluation of medical device calibration knowledge, perceived importance, and implementation support among healthcare professionals in Ghana

Supplemental material - Evaluation of medical device calibration knowledge, perceived importance, and implementation support among healthcare professionals in Ghana by Isaac Acquah, Benjamin Appiah Yeboah, Mawusi Gbemavor-Assonhe and Kojo Sam Micah in Health Informatics Journal.

Supplemental material

Supplemental material - Evaluation of medical device calibration knowledge, perceived importance, and implementation support among healthcare professionals in Ghana

Supplemental material - Evaluation of medical device calibration knowledge, perceived importance, and implementation support among healthcare professionals in Ghana by Isaac Acquah, Benjamin Appiah Yeboah, Mawusi Gbemavor-Assonhe and Kojo Sam Micah in Health Informatics Journal.