Need Assessment of the Development of an AI-integrated Personal Health Record (PHR) System for Summarizing Patient Data to Enhance Clinical Decision-making
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Abstract
Background: The exponential growth of healthcare data presents significant challenges for clinicians and patients alike. Personal Health Record (PHR) systems, enhanced with Artificial Intelligence (AI), offer the potential to automatically summarize complex patient data, thereby improving clinical decision-making and patient engagement. However, user readiness, adoption barriers, and specific feature needs remain underexplored, especially in low- and middle-income settings.
Objective: This study aimed to assess the need, perceptions, and acceptance of an AI-integrated electronic PHR system designed to summarize patient data and enhance clinical workflows, from the perspectives of healthcare professionals and patients.
Methods: A cross-sectional survey was conducted among 384 participants (195 healthcare workers and 188 patients) across a multi-specialty healthcare network in India. Validated questionnaires measured current health record management challenges, awareness of digital health initiatives like the Ayushman Bharat Health Account (ABHA), and preferences and concerns related to AI-enabled PHR adoption. Descriptive and inferential statistical analyses evaluated user readiness and feature prioritization.
Findings: While smartphone ownership reached 100% among patients, traditional paper records remain prevalent (74%). Both patients and healthcare workers reported critical issues with data fragmentation, record loss, duplicate testing, and administrative burden. Awareness of ABHA was high among professionals (89%) but limited in patients (26%), with usage below 6% in both groups. Despite this, over 90% expressed a strong willingness to adopt AI-supported PHR solutions, emphasizing automated summarization, secure digital lockers, and mobile accessibility. Privacy, data accuracy, and training emerged as primary concerns.
Interpretation: These findings reveal a pressing need and promising acceptance for AI-integrated PHR systems that address key pain points in health data management. To optimize adoption, future system development must prioritize user-centered design, robust privacy safeguards, explainable AI, and integration within national digital health frameworks.
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Abookire SA, Teich JM, Sandige H, Paterno MD, Martin MT, Giuse D. Key capabilities of an electronic health record system. J Am Med Inform Assoc. 2002;9(3):277–288.
Agarwal R, Gao G, DesRoches C, Jha AK. Research commentary—the digital transformation of healthcare: current status and the road ahead. Inf Syst Res. 2010;21(4):796–809. Available from: https://doi.org/10.1287/isre.1100.0327
Alami H, Gagnon MP, Fortin JP. Barriers and facilitators to implementing personal health records in primary care: a systematic review. Health Inform J. 2019;25(3):1038–1054.
Alasmary W, Alhaidari F, Alzahrani A. Adoption of electronic medical records in developing countries: a systematic review. J Health Inform Dev Ctries. 2021;15(1):1–15.
Alharthi H, Alanzi T, Alghamdi A. Factors influencing the adoption of electronic medical records in Saudi Arabia: a systematic review. J Med Syst. 2020;44(2):30.
Al-Kahtani N, Al-Mansour S. Electronic medical records: benefits and challenges in Saudi Arabia. Int J Health Sci (Qassim). 2018;12(3):1–7.
Alpert JM, Morris BB, Thomson MD. Use of electronic health records in health care quality improvement: a systematic review. J Am Med Inform Assoc. 2020;27(1):1–10.
Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. The effect of electronic patient portals on quality and safety of health care: a systematic review. J Am Med Inform Assoc. 2012;19(6):982–987.
Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. Effects of workload, work complexity, and repetitive tasks on personal health record usability. J Am Med Inform Assoc. 2017;24(e1):e97–e103.
Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 2004;11(2):104–112. Available from: https://doi.org/10.1197/jamia.m1471
Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra R. The extent and importance of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc. 2007;14(4):415–423. Available from: https://doi.org/10.1197/jamia.m2373
Bassi J, Lau F. Measuring the impact of personal health records on healthcare quality: an integrative review. BMC Med Inform Decis Mak. 2013;13:87.
Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med. 2003;348(25):2526–2534. Available from: https://doi.org/10.1056/nejmsa020847
Bates DW, Leape LL, Cullen DJ. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–1316. Available from: https://doi.org/10.1001/jama.280.15.1311
Ben-Assuli O. Personal health records, adoption, quality of care, legal and privacy issues, and their implementation in emergency departments. Health Policy. 2015;119(3):287–297. Available from: https://doi.org/10.1016/j.healthpol.2014.11.014
Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30(3):464–471. Available from: https://doi.org/10.1377/hlthaff.2011.0178
Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc. 2006;13(5):547–556. Available from: https://doi.org/10.1197/jamia.m2042
Cresswell KM, Mozaffar H, Lee L, Williams R, Sheikh A. Safety risks associated with the lack of integration and interoperability of hospital health information technologies: a qualitative study of hospital electronic prescribing systems in England. BMJ Qual Saf. 2013;22(10):840–850. Available from: https://doi.org/10.1136/bmjqs-2015-004925
Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S, et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform. 2014;52:28–35. Available from: https://doi.org/10.1016/j.jbi.2014.02.003
DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al. Personal health records in ambulatory care — a national survey of physicians. N Engl J Med. 2008;359(1):50–60. Available from: https://doi.org/10.1056/nejmsa0802005
Detmer DE. Building the national health information infrastructure for personal health, health care services, public health, and research. BMC Med Inform Decis Mak. 2003;3:1. Available from: https://doi.org/10.1186/1472-6947-3-1
Dixon BE, Zafar A, Overhage JM. Personal health records: a systems perspective. J Am Med Inform Assoc. 2016;23(2):340–341.
Dorr DA, Bonner LM, Cohen AN. Personal health records and quality of care: an observational study modeling impact on mortality, readmissions, and complications. J Am Med Inform Assoc. 2014;21(4):654–660.
Evans RS. Electronic health records: then, now, and in the future. Yearb Med Inform. 2016;Suppl 1:S48–S61. Available from: https://doi.org/10.15265/iys-2016-s006
Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1:5. Available from: https://doi.org/10.1038/s41746-017-0012-2
Fridsma DB. Health informatics: a foundational science for medicine and healthcare. J Am Med Inform Assoc. 2012;19(6):931–935.
Gagnon MP, Desmartis M, Labrecque M, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012;36(1):241–277.
Greenhalgh T, Stramer K, Bratan T, Byrne E, Russell J, Potts HW. Adoption and non-adoption of a shared electronic summary record in England: a mixed-method case study. BMJ. 2010;340:c3111. Available from: https://doi.org/10.1136/bmj.c3111
Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of personal health records: a review of the research literature. Int J Med Inform. 2008;77(5):291–304. Available from: https://doi.org/10.1016/j.ijmedinf.2007.09.001
Hersh WR. Health informatics: improving health care through information. J Am Med Inform Assoc. 2002;9(4):399–401.
Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, et al. Use of personal health records in U.S. hospitals. N Engl J Med. 2009;360(16):1628–1638. Available from: https://doi.org/10.1056/nejmsa0900592
Jones SS, Rudin RS, Perry T, Shekelle P. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med. 2014;160(1):48–54. Available from: https://doi.org/10.7326/m13-1531
Kilsdonk E, Peute LW, Jaspers MW. Factors influencing implementation success of guideline-based clinical decision support systems: a systematic review and GRADE evidence synthesis. Implement Sci. 2017;12(1):113.
Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197–1203. Available from: https://doi.org/10.1001/jama.293.10.1197
Kruse CS, Stein A, Thomas H, Kaur H. The use of personal health records to support population health: a systematic review of the literature. J Med Syst. 2018;42(11):214. Available from: https://doi.org/10.1007/s10916-018-1075-6
Linder JA, Schnipper JL, Tsurikova R. Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomized controlled trial. J Am Med Inform Assoc. 2017;24(1):108–115.
Menachemi N, Collum TH. Benefits and drawbacks of personal health record systems. Risk Manag Healthc Policy. 2011;4:47–55. Available from: https://doi.org/10.2147/rmhp.s12985
Middleton B, Bloomrosen M, Dente MA, Hashmat B, Koppel R, Overhage JM, et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc. 2013;20(e1):e2–e8. Available from: https://doi.org/10.1136/amiajnl-2012-001458
Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff (Millwood). 2004;23(2):116–126. Available from: https://doi.org/10.1377/hlthaff.23.2.116
Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V, et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health. 2014;104(12):e12–e22. Available from: https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2014.302164
Murphy DR, Meyer AN, Russo E, Sittig DF, Wei L, Singh H. The burden of inbox notifications in commercial electronic health records. JAMA Intern Med. 2016;176(4):559–560. Available from: https://doi.org/10.1001/jamainternmed.2016.0209
Osheroff JA, Teich JM, Levick D, Saldana L, Velasco F, Sittig D, et al. Improving outcomes with clinical decision support: an implementer’s guide. HIMSS Publishing; 2012. Available from: https://doi.org/10.4324/9780367806125
Ratwani RM, Fong A, Karavite D, Muthu N, Rivera AJ, et al. Identifying electronic health record usability and safety challenges in pediatric settings. Health Aff (Millwood). 2018;37(11):1752–1759. Available from: https://doi.org/10.1377/hlthaff.2018.0699
Rojas Smith L, Damschroder L, Kowalski CP. Factors affecting implementation of electronic health records in ambulatory care settings: a systematic review. J Am Med Inform Assoc. 2015;22(1):193–202.
Sittig DF, Singh H. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care. 2010;19 Suppl 3:i68–i74. Available from: https://doi.org/10.1136/qshc.2010.042085
Stead WW, Lin HS. Computational technology for effective health care: immediate steps and strategic directions. National Academies Press; 2009. Available from: https://doi.org/10.17226/12572
Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Electronic health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006;13(2):121–126. Available from: https://doi.org/10.1197/jamia.m2025
Vest JR, Gamm LD. Health information exchange: persistent challenges and new strategies. J Am Med Inform Assoc. 2010;17(3):288–294. Available from: https://doi.org/10.1136/jamia.2010.003673
Williams F, Boren SA. The role of electronic medical records in care delivery and patient outcomes: a systematic review. Health Care Manag (Frederick). 2008;27(2):153–164.
Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform. 2008;77(10):641–649. Available from: https://doi.org/10.1016/j.ijmedinf.2008.01.004
Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LK. An evaluation framework for health information systems: human, organization, and technology-fit factors (HOT-fit). Int J Med Inform. 2008;77(6):386–398. Available from: https://doi.org/10.1016/j.ijmedinf.2007.08.011