The pandemic increased pressure on healthcare services and forced limited care in all health facilities to ensure the care of all patients. Telepharmacy appears as an alternative to the remote pharmacy practice approach through information and communication technologies, but there are no comprehensive tools to measure pharmacists’ knowledge, perception, and readiness to implement telepharmacy. This study developed and validated a questionnaire version of the Knowledge, Perceptions, and Readiness of Telepharmacy (KPR-TP) for pharmacists.
Methods
The KPR-TP assessed three domains: knowledge, perception, and readiness. Its factor structure, reliability, and validity were assessed using 7730 pharmacists from 34 Indonesian provinces. The validity of the model's three-factor structure was assessed using confirmatory factor analysis. Cronbach's alpha and composite reliability were used to validate the consistency of our factors, whereas convergent and discriminant validity established significant relationships between them.
Results
The goodness-of-fit index indicated that the model was economical and reasonable. Furthermore, the correlation between the three domains revealed a significant positive relationship. KRP-TP is a viable instrument for assessing pharmacists’ perceptions of telepharmacy in Indonesia.
Conclusion
Overall, we discovered that our questionnaire contains critical constructs for assessing a pharmacist's knowledge, perception, and level of readiness to implement telepharmacy. This study will help pharmacists identify appropriate strategies for skill improvement.
PoudelANissenLM. Telepharmacy: a pharmacist’s perspective on the clinical benefits and challenges. Integr Pharm Res Pract2016; 5: 75–82.
2.
HuangJAHartantiIRColinMN,et al.Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: recent updates and challenges. Digit Health2022; 8: 20552076221100630.
3.
CaseyMMSorensenTDEliasW,et al.Current practices and state regulations regarding telepharmacy in rural hospitals. Am J Health Syst Pharm2010; 67: 1085–1092.
4.
MuflihSMAl-AzzamSAbuhammadS,et al.Pharmacists’ experience, competence and perception of telepharmacy technology in response to COVID-19. Int J Clin Pract2021; 75: e14209.
5.
MuhammadKBarakaMAShahSS, et al.Exploring the perception and readiness of pharmacists towards telepharmacy implementation; a cross sectional analysis. PeerJ2022; 10: e13296.
6.
NittariGSavvaDTomassoniD,et al.Telemedicine in the COVID-19 Era: a narrative review based on current evidence. Int J Environ Res Public Health2022; 19: 5101.
7.
NittariGKhumanRBaldoniS, et al.Telemedicine practice: review of the current ethical and legal challenges. Telemed E-Health2020; 26: 1427–1437.
8.
HoINielsenLJacobsgaardH,et al.Chat-based telepharmacy in Denmark: design and early results. Int J Pharm Pract2015; 23: 61–66.
9.
McFarlandR. Telepharmacy for remote hospital inpatients in north-west Queensland. J Telemed Telecare2017; 23: 861–865.
10.
BaldoniSAmentaFRicciG. Telepharmacy services: present status and future perspectives: a review. Medicina (B Aires)2019; 55: 327.
11.
MohiuddinANasirullahM. Scope of tele-pharmacists in pandemic situations of Bangladesh. Curr Tr Clin Med Sci2020; 1: 1–5.
TjiptoatmadjaNNAlfianSD. Knowledge, perception, and willingness to use telepharmacy among the general population in Indonesia. Front Public Health2022; 10: 825554.
14.
PatelK. Assessment of knowledge, attitude, perception of pharmacy students towards telepharmacy. Appl Res Projects2021: 75. https://doi.org/10.21007/chp.hiim.0072.
15.
JandovitzNLiHWattsB,et al.Telemedicine pharmacy services implementation in organ transplantation at a metropolitan academic medical center. Digit Health2018; 4: 2055207618789322.
16.
OmboniSMancinelliARizziF,et al.Group T (TEleMonitoring of blood P in L phARmacies) P. Telemonitoring of 24-h blood pressure in local pharmacies and blood pressure control in the community: the templar project. Am J Hypertens2019; 32: 629–639.
17.
BaldoniSPallottaGTrainiE,et al.A survey on feasibility of telehealth services among young Italian pharmacists. Pharm Pract Granada2020; 18: 1926.
18.
ZamanzadehVGhahramanianARassouliM,et al.Design and implementation content validity study: development of an instrument for measuring patient-centered communication. J Caring Sci2015; 4: 165–178.
19.
ElnaemMHAkkawiMEAl-ShamiAK,et al.Telepharmacy knowledge, perceptions, and readiness among future Malaysian pharmacists amid the COVID-19 pandemic. Ind J Pharm Educ Res2022; 56: 9–16.
20.
Margusino-FramiñánLFernández-LlamazaresCMNegro-VegaE, et al.Outpatients’ opinion and experience regarding telepharmacy during the COVID-19 pandemic: the Enopex project. J Multidiscip Healthc2021; 14: 3621.
21.
AlexanderEButlerCDDarrA, et al.ASHP Statement on telepharmacy. Am J Health Syst Pharm2017; 74: e236–e241.
22.
AyreCScallyAJ. Critical values for Lawshe’s content validity ratio: revisiting the original methods of calculation. Meas Eval Couns Dev2014; 47: 79–86.
23.
LynnMR. Determination and quantification of content validity. Nurs Res1986; 35: 382–386.
24.
KockN. Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. Int J E-Collab2014; 10: 1–13.
25.
HairJFBlackWCJBabinB,et al.Multivariate Data Analysis. 7th Edition. Saddle River, NJ: Prentice Hall Upper, 2009.
26.
TaberKS. The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Res Sci Educ2018; 48: 1273–1296.
27.
HairJFRisherJJSarstedtM,et al.When to use and how to report the results of PLS-SEM. Eur Bus Rev2019; 31: 2–24.
28.
RaithelSSarstedtMScharfS,et al.On the value relevance of customer satisfaction. Multiple drivers and multiple markets. J Acad Mark Sci2012; 40: 509–525.
29.
HenselerJSarstedtM. Goodness-of-fit indices for partial least squares path modeling. Comput Stat2013; 28: 565–580.
30.
RiouJGuyonHFalissardB. An introduction to the partial least squares approach to structural equation modelling: a method for exploratory psychiatric research. Int J Methods Psychiatr Res2016; 25: 220–231.
31.
BatterhamAMHopkinsWG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform2006; 1: 50–57.
32.
FornellCLarckerDF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res1981; 18: 39–50.
33.
ShmueliGKoppiusOR. Predictive analytics in information systems research. MIS Q Manag Inf Syst2011; 35: 553–572.
34.
RigdonEE. Rethinking partial least squares path modeling: in praise of simple methods. Long Range Plann2012; 45: 341–358.
35.
Hair JrJFBlackWCBabinBJ,et al.Multivariate data analysis. Annabel Ainscow2018: 95–120.
36.
BirukKAbetuE. Knowledge and attitude of health professionals toward telemedicine in resource-limited settings: a cross-sectional study in North West Ethiopia. J Healthc Eng2018: 2389268. https://doi.org/10.1155/2018/2389268.
37.
AyatollahiHSarabiFZPLangarizadehM. Clinicians’ knowledge and perception of telemedicine technology. Perspect Health Inf Manag2015; 12: 1c.
38.
HusinMRahmanNABujangMA, et al.Translation and validation of the questionnaire on acceptance to telemedicine from the technology acceptance model (TAM) for use in Malaysia. BioMed Res Int2022: 9123887. https://doi.org/10.1155/2022/9123887.
39.
AlnajraniRHAlnajraniNRAldakheelFS, et al.An assessment of the knowledge, perception, and willingness to use telepharmacy services among the general public in the Kingdom of Saudi Arabia. Cureus2022; 14: e31769.
