Meta-Analysis the Effect of Electronic Health Record Utilization on Mortality and Readmission

Desi Syahbaniar, Didik Gunawan Tamtomo, Bhisma Murti


Background: The development of technology shows a very rapid development, especially in the health sector. One example of the use of technology in the health sector is the electronic health record. Electronic health records provide several benefits, one of which can reduce mortality and readmission rates in patients. The purpose of this study was to estimate the effect of electronic health records on mortality and readmission rates by metaanalysis.
Subjects and Method: This was a metaanalysis study using PRISMA flowchart guidelines. The article search process was carried out between 20112022 using databases from PubMed, Google Scholar, ProQuest, Science Direct and Scopus. The PICO formula used is P = patients with asthma. I= using internetbased selfmanagement. C= without using internetbased selfmanagement. O= asthma control. Article searches were performed using the keywords “mhealth” OR “mobile health” OR “telemedicine” AND “self management” AND “asthma control” OR “asthma treatment” AND “asthma control”. The inclusion criteria were full paper articles with randomized controlled trial study design, articles using English, the intervention provided was the application of internetbased selfmanagement, and the outcome was asthma control. Based on the database, there were 9 articles that met the inclusion criteria. The analysis was carried out using Revman 5.3 software.
Results: A total of 13 articles spread across 2 continents, namely Asia (Taiwan, Singapore, and South Korea) and North America (South America). Articles reviewed in the metaanalysis showed that electronic health records had an effect on reducing mortality by 0.74 times compared to those without using electronic health records (aOR= 0.74; 95% CI= 0.64 to 0.86; p<0.001). In addition, it was also found that electronic health records had an effect on reducing readmission by 0.77 times compared to without using electronic health records (aOR= 0.77; CI 95%= 0.62 to 0.95; p= 0.010)
Conclusion: The application of electronic health records has an effect on reducing mortality and readmission rates.
Keywords: Electronic health record, mortalitas, readmission

Correspondence: Desi Syahbaniar. Masters Program in Public Health, universitas Sebelas Maret. Jalan Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: Mobile: +6287708465646

Journal of Health Policy and Management (20220, 07(02): 103-111

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Agha L (2014). The effects of health information technology on the costs and quality of medical care. J Health Econ. 34(2): 19–30. doi: 10.1016/j.jhealeco.2013.12.005.

Amato MG, Salazar A, Hickman TTT, Quist AJL, Volk LA, Wright A, McEvoy D, et al. (2017). Computerized prescriber order entryrelated patient safety reports: Analysis of 2522 medication errors. J Am Med Informatics Associ. 24(2): 316–322. doi: 10.1093/jamia/ocw125.

Blecker S, Goldfeld K, Shine D, Austrian J, Braithwaite R, Radford M, Gourevitch M (2015). Electronic health record utilization, intensity of hospital care and patient outcomes. Am J Med. 127(3): 216–221. doi: 10.1016/j.amjmed.2013.11.010.

Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A, Ricciardi W, Specchia ML (2016). The impact of electronic health records on healthcare quality: A systematic review and metaanalysis. Eur J Pub Health. 26(1): 60–64. doi: 10.1093/eurpub/ckv122.

Encinosa W, Bae J (2012). How Can We Bend the Cost Curve? Health Information Technology and Its Effects on Hospital Costs , Outcomes, and Patient Safety’, Inquiry. 48(4): 288– 303. doi: 10.5034/inquiryjrnl_48.04.02.

Flatow VH, Ibragimova N, Divino CM, Eshak DSA, Twohig BC, BassilyMarcus AM, KohliSeth R (2015). Quality outcomes in the surgical intensive care unit after electronic health record implementation. Appl Clin Inform. 6(4): 611–618. doi: 10.4338/ACI201504RA0044.

Han JE, Rabinovich M, Abraham P, Satyanarayana P, Liao TV, Udoji TN, Cotsonis GA, et al. (2016). Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors. Am J Med Sci. 351(6): 576–581. doi: 10.1016/j.amjms.2016.01.026.

Hwang JI, Park HA, Bakken S (2012). Impact of a physician’s order entry (POE) system on physicians’ ordering patterns and patient length of stay. Int J Med Inform. 65(3): 213–223. doi: 10.1016/S13865056(02)000448.

Krive J, Shoolin JS, Zink SD (2015). Effectiveness of Evidencebased Pneumonia CPOE Order Sets Measured by Health Outcomes. Online J Pub Health Inform. 7(2): 1–15. doi: 10.5210/ojphi.v7i2.5527.

Kruse CS, Stein A, Thomas H, Kaur H (2018). The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature. J Med Syst, 42(11). doi: 10.1007/s1091601810756.

Lin HL, Wu DC, Cheng SM, Chen CJ, Wang M C, Cheng CA (2020). Association between Electronic Medical Records and Healthcare Quality. Medicine. 99(31): e21182. doi: 10.1097/MD.0000000000021182.

McGregor J, Weekes E, Forrest E, Furuno J, Harris A (2015). Impact of a Computerized Clinical Decision Support System on Reducing Inappropriate Antimicrobial Use: A Randomized Controlled Trial. Online J Public Health Inform. 13(4): 378–384. doi:10.1197/jamia.M2049.Introduction.

Moja L, Polo Friz H, Capobussi M, Kwag K, Banzi R, Ruggiero F, GonzálezLorenzo M, et al. (2019). Effectiveness of a hospitalbased computerized decision support system on clinician recommendations and patient outcomes: A randomized clinical trial.JAMA Netw Open, 2(12): 1–16. doi: 10.1001/jamanetworkopen.2019.17094.

Ndifon L, Edwards JE, Halawi L (2016). Impact of Electronic Health Records on Patient Outcomes. Issues Inf Syst. 17(2): 187–196. doi: 10.48009/4_iis_2016_187196.

Paul M, Andreassen S, Tacconelli E, Nielsen AD, Almanasreh N, Frank U, Cauda R, et al. (2016). Improving empirical antibiotic treatment using TREAT , a computerized decision support system: cluster randomized trial. J Antimicrob Chemother. 58(2):1238–1245. doi: 10.1093/jac/dkl372.

Sudarmaji WP, Sholihin S, Permana RA, Soares A, Nugraha YA (2020). A Clinical Decision Support System as a Tool to Improve the Accuracy of Nursing Diagnoses. J Ners, 14(3): 388. doi: 10.20473/jn.v14i3.17171.

Wilson FP, Shashaty M, Testani J, Aqeel I, Borovskiy Y, Ellenberg SS, Feldman HI, et al. (2015). Automated, electronic alerts for acute kidney injury: a singleblind, parallelgroup, randomised controlled trial. Lancet (London, English) J. 385(9981):1966–1974. doi: 10.1016/S01406736(15)602665.

Wilson FP, Martin M, Yamamoto Y, Partridge C, Moreira E, Arora T, Biswas A, et al. (2021). Electronic health record alerts for acute kidney injury: multicenter , randomized clinical trial. BMJ. 37(2): 1–10. doi: 10.1136/bmj.m4786.

Yanamadala S, Morrison D, Curtin C, McDonald K, HernandezBoussard T (2016). Electronic health records and quality of care an observational study modeling impact on mortality, readmissions, and complications. Med (United States). 95(19): 1–6. doi:10.1097/MD.0000000000003332.


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