504
Views
1
CrossRef citations to date
0
Altmetric
REVIEW

Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future

ORCID Icon, ORCID Icon, & ORCID Icon
Pages 2171-2183 | Received 29 Jun 2023, Accepted 05 Nov 2023, Published online: 22 Dec 2023

References

  • Age UK. Briefing: Health and Care of Older People in England 2017; 2017.
  • Vedel I, Akhlaghpour S, Vaghefi I, Bergman H, Lapointe L. Health information technologies in geriatrics and gerontology: a mixed systematic review. J Am Med Informatics Assoc. 2013;20(6):1109–1119. doi:10.1136/amiajnl-2013-001705
  • Johnson KB, Neuss MJ, Detmer DE. Electronic health records and clinician burnout: a story of three eras. J Am Med Informatics Assoc. 2021;28(5):967–973. doi:10.1093/jamia/ocaa274
  • Latif J, Xiao C, Tu S, Rehman SU, Imran A, Bilal A. Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: a Complete Review. IEEE Access. 2020;8:150489–150513. doi:10.1109/access.2020.3016782
  • Campanella P, Lovato E, Marone C, et al. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Public Health. 2015;26(1):60–64. doi:10.1093/eurpub/ckv122
  • Dendere R, Slade C, Burton-Jones A, Sullivan C, Staib A, Janda M. Patient Portals Facilitating Engagement With Inpatient Electronic Medical Records: a Systematic Review. J Med Internet Res. 2019;21(4):e12779–e12779. doi:10.2196/12779
  • Evans RS. Electronic Health Records: then, Now, and in the Future. Yearb Med Inform. 2016;Suppl 1(Suppl 1):S48–S61. doi:10.15265/IYS-2016-s006
  • Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388(13):1201–1208. doi:10.1056/nejmra2302038
  • Janett RS, Yeracaris PP. Electronic Medical Records in the American Health System: challenges and lessons learned. Cien Saude Colet. 2020;25(4):1293–1304. doi:10.1590/1413-81232020254.28922019
  • Mukaetova-Ladinska EB, Harwood T, Maltby J. Artificial Intelligence in the healthcare of older people. Arch Psychiatry Ment Heal. 2020;4(1):7–13. doi:10.29328/journal.apmh.1001011
  • Singh N, Lawrence K, Sinsky C, Mann DM. Digital Minimalism — an Rx for Clinician Burnout. N Engl J Med. 2023;388(13):1158–1159. doi:10.1056/nejmp2215297
  • Todd OM, Burton JK, Dodds RM, et al. New Horizons in the use of routine data for ageing research. Age Ageing. 2020;49(5):716–722. doi:10.1093/ageing/afaa018
  • Uslu A, Stausberg J. Value of the Electronic Medical Record for Hospital Care: update From the Literature. J Med Internet Res. 2021;23(12):e26323. doi:10.2196/26323
  • Stypińska J, Franke A. AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population. Front Sociol. 2023;7:1038854. doi:10.3389/fsoc.2022.1038854
  • Peltan ID, Beesley SJ, Brown SM. Can Big Data Deliver on Its Promises?—Leaps but Not Bounds. JAMA Netw Open. 2018;1(8):e185694. doi:10.1001/jamanetworkopen.2018.5694
  • Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018;391(10132):1775–1782. doi:10.1016/S0140-6736(18)30668-8
  • Mak JKL, Hägg S, Eriksdotter M, et al. Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden. Journals Gerontol Ser A. 2022;77(11):2311–2319. doi:10.1093/gerona/glac069
  • Mak JKL, Religa D, Jylhävä J. Automated frailty scores: towards clinical implementation. Aging. 2023;15(11):4571–4573. doi:10.18632/aging.204815
  • Takenouchi K, Yuasa K, Shioya M, et al. Development of a new seamless data stream from EMR to EDC system using SS-MIX2 standards applied for observational research in diabetes mellitus. Learn Heal Syst. 2018;3(1):e10072–e10072. doi:10.1002/lrh2.10072
  • Kokorelias KM, Danieli E, Dunn S, Feldman S, Ryan DP, Sadavoy J. The DWQ-EMR Embedded Tool to Enhance the Family Physician-Caregiver Connection: a Pilot Case Study. Geriatrics. 2021;6(1):29. doi:10.3390/geriatrics6010029
  • Wilkinson T, Ly A, Schnier C, et al. Identifying dementia cases with routinely collected health data: a systematic review. Alzheimer’s Dement. 2018;14(8):1038–1051. doi:10.1016/j.jalz.2018.02.016
  • Dormosh N, Heymans MW, van der Velde N, et al. External Validation of a Prediction Model for Falls in Older People Based on Electronic Health Records in Primary Care. J Am Med Dir Assoc. 2022;23(10):1691–1697.e3. doi:10.1016/j.jamda.2022.07.002
  • Reuben DB, Hackbarth AS, Wenger NS, Tan ZS, Jennings LA. An Automated Approach to Identifying Patients with Dementia Using Electronic Medical Records. J Am Geriatr Soc. 2017;65(3):658–659. doi:10.1111/jgs.14744
  • Liong-Rung L, Hung-Wen C, Ming-Yuan H, et al. Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea. Front Med. 2022;9:893208. doi:10.3389/fmed.2022.893208
  • Kim JH, Hua M, Whittington RA, et al. A machine learning approach to identifying delirium from electronic health records. JAMIA Open. 2022;5(2):ooac042. doi:10.1093/jamiaopen/ooac042
  • Luo J, Liao X, Zou C, et al. Identifying Frail Patients by Using Electronic Health Records in Primary Care: current Status and Future Directions. Front Public Heal. 2022;10:901068. doi:10.3389/fpubh.2022.901068
  • Park S, Kim AJ, Ah YM, et al. Prevalence and predictors of medication-related emergency department visit in older adults: a multicenter study linking national claim database and hospital medical records. Front Pharmacol. 2022;13:1009485. doi:10.3389/fphar.2022.1009485
  • Yoon K, Kim JT, Kwack WG, et al. Potentially Inappropriate Medication Use in Patients with Dementia. Int J Environ Res Public Health. 2022;19(18):11426. doi:10.3390/ijerph191811426
  • Brown T, Rowe TA, Lee JY, et al. Design of Behavioral Economic Applications to Geriatrics Leveraging Electronic Health Records (BEAGLE): a pragmatic cluster randomized controlled trial. Contemp Clin Trials. 2022;112:106649. doi:10.1016/j.cct.2021.106649
  • Polnaszek B, Gilmore-Bykovskyi A, Hovanes M, et al. Overcoming the Challenges of Unstructured Data in Multisite, Electronic Medical Record-based Abstraction. Med Care. 2016;54(10):e65–e72. doi:10.1097/MLR.0000000000000108
  • Floyd JS, Heckbert SR, Weiss NS, Carrell DS, Psaty BM. Use of administrative data to estimate the incidence of statin-related rhabdomyolysis. JAMA. 2012;307(15):1580–1582. doi:10.1001/jama.2012.489
  • Chen L, Li N, Zheng Y, et al. A novel semiautomatic Chinese keywords instrument screening delirium based on electronic medical records. BMC Geriatr. 2022;22(1):779. doi:10.1186/s12877-022-03474-w
  • Chowdhury M, Cervantes EG, Chan WY, Seitz DP. Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: a Systematic Review. Front Psychiatry. 2021;12:738466. doi:10.3389/fpsyt.2021.738466
  • Shao Y, Zeng QT, Chen KK, Shutes-David A, Thielke SM, Tsuang DW. Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records. BMC Med Inform Decis Mak. 2019;19(1):128. doi:10.1186/s12911-019-0846-4
  • McCarty CA, Chisholm RL, Chute CG, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011;4:13. doi:10.1186/1755-8794-4-13
  • Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45(3):353–360. doi:10.1093/ageing/afw039
  • Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357. doi:10.1136/bmj.j2099
  • Ford I, Norrie J. Pragmatic Trials. N Engl J Med. 2016;375(5):454–463. doi:10.1056/nejmra1510059
  • Wiemken TL, Kelley RR. Machine Learning in Epidemiology and Health Outcomes Research. Annu Rev Public Health. 2020;41(1):21–36. doi:10.1146/annurev-publhealth-040119-094437
  • Luo W, Phung D, Tran T, et al. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: a Multidisciplinary View. J Med Internet Res. 2016;18(12):e323–e323. doi:10.2196/jmir.5870
  • Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20):1920–1930. doi:10.1161/CIRCULATIONAHA.115.001593
  • Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: promises and Perils. Ann Surg. 2018;268(1):70–76. doi:10.1097/SLA.0000000000002693
  • Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. Am J Med. 2019;132(7):795–801. doi:10.1016/j.amjmed.2019.01.017
  • Ben Miled Z, Haas K, Black CM, et al. Predicting dementia with routine care EMR data. Artif Intell Med. 2020;102:101771. doi:10.1016/j.artmed.2019.101771
  • Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459–8486. doi:10.1007/s12652-021-03612-z
  • Naga Srinivasu P, Ahmed S, Alhumam A, Bhoi Kumar A, Fazal Ijaz M. An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images. Comput Mater Contin. 2021;69(3):3303–3319. doi:10.32604/cmc.2021.018472
  • Voets M, Møllersen K, Bongo LA. Reproduction study using public data of: development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS One. 2019;14(6):e0217541–e0217541. doi:10.1371/journal.pone.0217541
  • Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056
  • Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735–1743. doi:10.1038/s41591-021-01506-3
  • Rosen T, Zhang Y, Bao Y, et al. Can artificial intelligence help identify elder abuse and neglect? J Elder Abuse Negl. 2020;32(1):97–103. doi:10.1080/08946566.2019.1682099
  • Asan O, Xu J, Montague E. Dynamic Comparison of Physicians’ Interaction Style with Electronic Health Records in Primary Care Settings. J Gen Pract. 2013;2:1000137. doi:10.4172/2329-9126.1000137
  • Hayashi Y, Godai A, Yamada M, et al. Reduction in the numbers of drugs administered to elderly in-patients with polypharmacy by a multidisciplinary review of medication using electronic medical records. Geriatr Gerontol Int. 2016;17(4):653–658. doi:10.1111/ggi.12764
  • Lawrence JE, Cundall-Curry D, Stewart ME, Fountain DM, Gooding CR. The use of an electronic health record system reduces errors in the National Hip Fracture Database. Age Ageing. 2019;48(2):285–290. doi:10.1093/ageing/afy177
  • Arai S, Ishikawa T, Kato H, et al. Multidrug use positively correlates with high-risk prescriptions in the Japanese elderly: a longitudinal study. J Pharm Heal Care Sci. 2019;5:20. doi:10.1186/s40780-019-0150-6
  • Minaya-Freire A, Subirana-Casacuberta M, Puigoriol-Juvanteny E, Ramon-Aribau A. Pain management nursing practice assessment in older adults with dementia. Nurs Open. 2021;8(6):3349–3357. doi:10.1002/nop2.880
  • Munyisia E, Yu P, Hailey D. The effect of an electronic health record system on nursing staff time in a nursing home: a longitudinal cohort study. Australas Med J. 2014;7(7):285–293. doi:10.4066/AMJ.2014.2072
  • Melzer D, Tavakoly B, Winder RE, et al. Much more medicine for the oldest old: trends in UK electronic clinical records. Age Ageing. 2015;44(1):46–53. doi:10.1093/ageing/afu113
  • Wilkinson C, Weston C, Timmis A, Quinn T, Keys A, Gale CP. The Myocardial Ischaemia National Audit Project (MINAP). Eur Hear J - Qual Care Clin Outcomes. 2020;6(1):19–22. doi:10.1093/ehjqcco/qcz052
  • Liebovitz D. Next steps for electronic health records to improve the diagnostic process. Diagnosis. 2015;2(2):111–116. doi:10.1515/dx-2014-0070
  • Agency for Healthcare Research and Quality. Ambulatory Care Safety; 2019. Available from: https://psnet.ahrq.gov/primer/ambulatory-care-safety. Accessed June 21, 2023.
  • Bardach SH, Real K, Bardach DR. Perspectives of healthcare practitioners: an exploration of interprofessional communication using electronic medical records. J Interprof Care. 2017;31(3):300–306. doi:10.1080/13561820.2016.1269312
  • Sommerlad A, Perera G, Mueller C, et al. Hospitalisation of people with dementia: evidence from English electronic health records from 2008 to 2016. Eur J Epidemiol. 2019;34(6):567–577. doi:10.1007/s10654-019-00481-x
  • Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med. 2023;388(13):1233–1239. doi:10.1056/nejmsr2214184
  • Car J, Sheikh A, Wicks P, Williams MS. Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom. BMC Med. 2019;17(1):143. doi:10.1186/s12916-019-1382-x
  • Wang S, Bolling K, Mao W, et al. Technology to Support Aging in Place: older Adults’ Perspectives. Healthcare. 2019;7(2):60. doi:10.3390/healthcare7020060
  • Wangmo T, Lipps M, Kressig RW, Ienca M. Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders. BMC Med Ethics. 2019;20(1):98. doi:10.1186/s12910-019-0437-z
  • Chen S, Banks WA, Sheffrin M, Bryson W, Black M, Thielke SM. Identifying and categorizing spurious weight data in electronic medical records. Am J Clin Nutr. 2018;107(3):420–426. doi:10.1093/ajcn/nqx056
  • Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2):205395171667967. doi:10.1177/2053951716679679
  • Rosales A, Fernández-Ardèvol M. Structural Ageism in Big Data Approaches. Nord Rev. 2019;40(s1):51–64. doi:10.2478/nor-2019-0013
  • World Health Organization. Ageism in Artificial Intelligence for Health: WHO Policy Brief; 2022.
  • Park JH, Cho HE, Kim JH, et al. Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data. NPJ Digit Med. 2020;3:46. doi:10.1038/s41746-020-0256-0
  • Ford E, Sheppard J, Oliver S, Rooney P, Banerjee S, Cassell JA. Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records. BMJ Open. 2021;11(1):e039248–e039248. doi:10.1136/bmjopen-2020-039248
  • Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc. 2017;24(6):1052–1061. doi:10.1093/jamia/ocx030
  • Iluz T, Weiss A, Gazit E, et al. Can a Body-Fixed Sensor Reduce Heisenberg’s Uncertainty When It Comes to the Evaluation of Mobility? Effects of Aging and Fall Risk on Transitions in Daily Living. J Gerontol a Biol Sci Med Sci. 2016;71(11):1459–1465. doi:10.1093/gerona/glv049
  • Corradi JP, Thompson S, Mather JF, Waszynski CM, Dicks RS. Prediction of Incident Delirium Using a Random Forest classifier. J Med Syst. 2018;42(12):261. doi:10.1007/s10916-018-1109-0
  • Rahimian F, Salimi-Khorshidi G, Payberah AH, et al. Predicting the risk of emergency admission with machine learning: development and validation using linked electronic health records. PLoS Med. 2018;15(11):e1002695. doi:10.1371/journal.pmed.1002695
  • Jackson L, Saund J, Donnelly G. Improving the Documentation of DNACPR Decisions Following the Transition to Electronic Record Keeping. Age Ageing. 2021;50(Supplement_1):i12–i42. doi:10.1093/ageing/afab030.31
  • Middleton B, Bloomrosen M, Dente MA, et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Informatics Assoc. 2013:20(e1):e2–e8. doi:10.1136/amiajnl-2012-001458
  • Lin HL, Wu DC, Cheng SM, Chen CJ, Wang MC, Cheng CA. Association between Electronic Medical Records and Healthcare Quality. Medicine. 2020;99(31):e21182–e21182. doi:10.1097/MD.0000000000021182
  • Gardner RL, Cooper E, Haskell J, et al. Physician stress and burnout: the impact of health information technology. J Am Med Informatics Assoc. 2019;26(2):106–114. doi:10.1093/jamia/ocy145
  • Steinkamp J, Kantrowitz JJ, Airan-Javia S. Prevalence and Sources of Duplicate Information in the Electronic Medical Record. JAMA Netw Open. 2022;5(9):e2233348–e2233348. doi:10.1001/jamanetworkopen.2022.33348
  • Harry E, Sinsky C, Dyrbye LN, et al. Physician Task Load and the Risk of Burnout Among US Physicians in a National Survey. Jt Comm J Qual Patient Saf. 2021;47(2):76–85. doi:10.1016/j.jcjq.2020.09.011
  • Sinsky C, Colligan L, Li L, et al. Allocation of Physician Time in Ambulatory Practice: a Time and Motion Study in 4 Specialties. Ann Intern Med. 2016;165(11):753. doi:10.7326/m16-0961
  • Holmgren AJ, Downing NL, Tang M, Sharp C, Longhurst C, Huckman RS. Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use. J Am Med Informatics Assoc. 2022;29(3):453–460. doi:10.1093/jamia/ocab268
  • Flanagan ME, Saleem JJ, Millitello LG, Russ AL, Doebbeling BN. Paper- and computer-based workarounds to electronic health record use at three benchmark institutions. J Am Med Informatics Assoc. 2013;20(e1):e59–e66. doi:10.1136/amiajnl-2012-000982
  • Darmon D, Sauvant R, Staccini P, Letrilliart L. Which functionalities are available in the electronic health record systems used by French general practitioners? An assessment study of 15 systems. Int J Med Inform. 2014;83(1):37–46. doi:10.1016/j.ijmedinf.2013.10.004
  • Krist AH. Electronic health record innovations for healthier patients and happier doctors. J Am Board Fam Med. 2015;28(3):299–302. doi:10.3122/jabfm.2015.03.150097
  • Brault I, Therriault PY, St-Denis L, Lebel P. Implementation of interprofessional learning activities in a professional practicum: the emerging role of technology. J Interprof Care. 2015;29(6):530–535. doi:10.3109/13561820.2015.1021308
  • Grabenbauer L, Skinner A, Windle J. Electronic Health Record Adoption - Maybe It’s not about the Money: physician Super-Users. Electronic Health Records and Patient Care Appl Clin Inform. 2011;2(4):460–471. doi:10.4338/ACI-2011-05-RA-0033
  • Gooch P, Roudsari A. Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. J Am Med Informatics Assoc. 2011;18(6):738–748. doi:10.1136/amiajnl-2010-000033
  • Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Informatics Assoc. 2011;18(3):327–334. doi:10.1136/amiajnl-2011-000094
  • Menachemi N, Collum TH. Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy. 2011;4:47–55. doi:10.2147/RMHP.S12985
  • European Union (EU). General data protection regulation (GDPR). 2017. Available from: https://gdpr.eu/. Accessed May 29, 2023.
  • Mamra A, Sibghatullah AS, Ananta GP, Alazzam MB, Ahmed YH, Doheir M. Theories and factors applied in investigating the user acceptance towards personal health records: review study. Int J Healthc Manag. 2017;10(2):89–96. doi:10.1080/20479700.2017.1289439
  • Barros Pena B, Clarke RE, Holmquist LE, Vines J. Circumspect Users: older Adults as Critical Adopters and Resistors of Technology. Proc 2021 CHI Conf Hum Factors Comput Syst. 2021. doi:10.1145/3411764.3445128
  • Zhang X, Xu X, Cheng J. WeChatting for Health: what Motivates Older Adult Engagement with Health Information. Healthcare. 2021;9(6):751. doi:10.3390/healthcare9060751
  • Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health. 2021;21(1):1556. doi:10.1186/s12889-021-11623-w
  • Wang X, Zhao YC. Understanding older adults’ intention to use patient-accessible electronic health records: based on the affordance lens. Front Public Heal. 2022;10:1075204. doi:10.3389/fpubh.2022.1075204
  • Nurgalieva L, Cajander A, Moll J, Åhlfeldt RM, Huvila I, Marchese M. ‘I do not share it with others. No, it’s for me, it’s my care’: on sharing of patient accessible electronic health records. Health Informatics J. 2020;26(4):2554–2567. doi:10.1177/1460458220912559
  • Eriksson-Backa K, Hirvonen N, Enwald H, Huvila I. Enablers for and barriers to using My Kanta – a focus group study of older adults’ perceptions of the National Electronic Health Record in Finland. Informatics Heal Soc Care. 2021;46(4):399–411. doi:10.1080/17538157.2021.1902331
  • Faiola A, Papautsky EL, Isola M. Empowering the Aging with Mobile Health: a mHealth Framework for Supporting Sustainable Healthy Lifestyle Behavior. Curr Probl Cardiol. 2019;44(8):232–266. doi:10.1016/j.cpcardiol.2018.06.003
  • West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. J Am Med Informatics Assoc. 2015;22(2):330–339. doi:10.1136/amiajnl-2014-002955
  • Bauermeister S, Orton C, Thompson S, et al. The Dementias Platform UK (DPUK) Data Portal. Eur J Epidemiol. 2020;35(6):601–611. doi:10.1007/s10654-020-00633-4
  • Brayne C, Davis D. Making Alzheimer’s and dementia research fit for populations. Lancet. 2012;380(9851):1441–1443. doi:10.1016/S0140-6736(12)61803-0
  • Benchimol EI, Smeeth L, Guttmann A, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885–e1001885. doi:10.1371/journal.pmed.1001885
  • Liu X, Rivera SC, Moher D. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164–m3164. doi:10.1136/bmj.m3164
  • Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Heal. 2020;2(10):e549–e560. doi:10.1016/S2589-7500(20)30219-3
  • Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Informatics Assoc. 2014;21(6):1053–1059. doi:10.1136/amiajnl-2013-002578
  • Elias B, Barginere M, Berry PA, Selleck CS. Implementation of an electronic health records system within an interprofessional model of care. J Interprof Care. 2015;29(6):551–554. doi:10.3109/13561820.2015.1021001
  • Gold JA, Tutsch ASR, Gorsuch A, Mohan V. Integrating the Electronic Health Record into high-fidelity interprofessional intensive care unit simulations. J Interprof Care. 2015;29(6):562–563. doi:10.3109/13561820.2015.1063482
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7
  • Lee JL, Matthias MS, Menachemi N, Frankel RM, Weiner M. A critical appraisal of guidelines for electronic communication between patients and clinicians: the need to modernize current recommendations. J Am Med Informatics Assoc. 2018;25(4):413–418. doi:10.1093/jamia/ocx089