Alert Features for Healthcare in Increasing Hospital Revenue from DRG Reimbursement: A Systematic Literature Review
DOI:
https://doi.org/10.47701/3am59z25Keywords:
medical records, alert, insurance claims, EHR, documentation quality, reimbursementAbstract
The inadequate quality of medical records and the
elevated rate of insurance claim denials worldwide have
necessitated the incorporation of alert functionalities into
Electronic Health Record (EHR) systems to enhance
documentation precision and reimbursement
effectiveness. This research seeks to evaluate the impact
of alerts on healthcare professionals' ability to enhance
the quality of medical records (completeness, accuracy,
timeliness) and insurance claim results through a
Systematic Literature Review (SLR) following PRISMA 2020
guidelines. Out of 538 studies sourced from PubMed,
Scopus, and Web of Science, 14 were selected based on
strict inclusion criteria. The synthesis of results reveals that
alerts—particularly those integrated with clinical decision
support—substantially enhance documentation quality
and decrease claim rejections. The successful execution of
this integration relies on EHR compatibility, management
backing, user education, and the alleviation of alert
fatigue. These results offer an empirical foundation for
optimizing hospital revenue by improving the quality of
medical records and the efficiency of claims, although
additional research is required in the contexts of
developing countries and for evaluating cost-effectiveness.
References
Ahmed, D., Yuantao, X., & Bhutta, U. S. (2021). Insurers’ risk management as a business process: A prospective competitive advantage or not? European Journal of Management and Business Economics.
Amroze, A., Field, T. S., Fouayzi, H., Sundaresan, D., Burns, L., Garber, L., Sadasivam, R. S., Mazor, K. M., Gurwitz, J. H., & Cutrona, S. L. (2019). Use of electronic health record access and audit logs to identify physician actions following noninterruptive alert opening: Descriptive study. JMIR Medical Informatics, 7(1), e12650.
Bae, H., & Hwang, Y. (2024). Economic evaluation of remote monitoring for implantable cardiac devices: Evidence from a remote-care study. ClinicoEconomics and Outcomes Research, 16, 697–705.
Baum, G., Sullivan, M., Kohne, L., Seaberry, C., & Powers, T. (2022). Reducing inpatient psychiatric unit denials. Professional Case Management, 27(6), 288–294.
Behrendt, C.-A., Schwaneberg, T., Hischke, S., Müller, T., Petersen, T., Marschall, U., Debus, S., & Kriston, L. (2019). Data privacy compliant validation of health insurance claims data: The IDOMENEO approach. Das Gesundheitswesen, 82(S 02), S94–S100.
Benthin, C., Pannu, S., Khan, A., & Gong, M. (2016). The nature and variability of automated practice alerts derived from electronic health records in a U.S. nationwide critical care research network. Annals of the American Thoracic Society, 13(10), 1784–1788.
Brien, R., Volpe, I., Grigg, J., Lyons, T., Hughes, C., McKinnon, G., Tzanetis, S., Crawford, S., Eade, A., Lee, N., & Barratt, M. J. (2023). Co-designing drug alerts for health and community workers for an emerging early warning system in Victoria, Australia. Harm Reduction Journal, 20(1).
Budd, J. (2023). Burnout related to electronic health record use in primary care. Journal of Primary Care & Community Health, 14.
Chew, D., Piccini, J., Frazier-Mills, C., Michalski, J., & Varma, N. (2024). Alert-based remote monitoring: A model for increased reimbursement to meet device clinic workload while achieving overall health system cost savings. Heart Rhythm.
Crovini, C., Ossola, G., & Britzelmaier, B. (2020). How to reconsider risk management in SMEs? An advanced, reasoned, and organised literature review. European Management Journal.
Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y. (2020). A secure AI-driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access, 8, 58546–58558.
Fazaeli, S., Meraji, M., Yousefi, M., Jamali, J., & Agahi, M. (2020). Comparative study of quantitative analysis indicators of inpatient medical records: Providing analysis tool. Journal of Modern Medical Information Sciences, 6(1), 45–57.
Fourquet, J., Zavala, D. E., Missmer, S., Bracero, N., Romaguera, J., & Flores, I. (2019). Disparities in healthcare services in women with endometriosis with public vs. private health insurance. American Journal of Obstetrics and Gynecology, 221(6), 623.e1–623.e11.
Fraser, H. S. F., Mugisha, M., Bacher, I., Ngenzi, J. L., Seebregts, C., Umubyeyi, A., & Condo, J. (2024). Factors influencing data quality in electronic health record systems in 50 health facilities in Rwanda and the role of clinical alerts: Cross-sectional observational study. JMIR Public Health and Surveillance, 10, e49127.
Funke, L., Canal, C., Ziegenhain, F., Pape, H. C., & Neuhaus, V. (2021). Does insurance status influence in-hospital outcome? A retrospective assessment in 30,175 surgical trauma patients in Switzerland. European Journal of Trauma and Emergency Surgery, 48(2), 1121–1128.
Horowitz, H. (2021). Use of an electronic medical record alert. Clinical Infectious Diseases.
Iqbal, F. M., Aggarwal, R., Joshi, M., King, D., Martin, G., Khan, S., Wright, M., Ashrafian, H., & Darzi, A. (2024). Barriers to and facilitators of key stakeholders influencing successful digital implementation of remote monitoring solutions: Mixed methods analysis. JMIR Human Factors, 11, e49769.
Jindal, S. K., & Raziuddin, F. (2018). Electronic medical record use and perceived medical error reduction. International Journal of Quality and Service Sciences, 10(1), 84–95.
Jung, S. Y., Hwang, H., Lee, K., Lee, H. Y., Kim, E., Kim, M., & Cho, I. Y. (2020). Barriers and facilitators to implementation of medication decision support systems in electronic medical records: Mixed methods approach. JMIR Medical Informatics.
Kinshella, M. L. W., Walker, C. R., Hiwa, T., Vidler, M., Nyondo-Mipando, A. L., Dube, Q., Goldfarb, D. M., & Kawaza, K. (2020). Barriers and facilitators to implementing bubble CPAP to improve neonatal health in sub-Saharan Africa: A systematic review. Public Health Reviews, 41(1).
Kumar, N., Akosman, I., Mortenson, R., Xu, G., Kumar, A., Mostafa, E., Rivlin, J., De La Garza Ramos, R., Krystal, J., Eleswarapu, A., Yassari, R., & Fourman, M. S. (2024). Disparities in postoperative complications and perioperative events based on insurance status following elective spine surgery: A systematic review and meta-analysis. North American Spine Society Journal, 17, 100315.
Lai, Y., & Afseth, J. (2019). A review of the impact of utilising electronic medical records for clinical research recruitment. Clinical Trials, 16, 194–203.
Lazzarino, R., Borek, A. J., Honeyford, K., Welch, J., Brent, A. J., Kinderlerer, A., Cooke, G., Patil, S., Gordon, A., Glampson, B., Goodman, P., Ghazal, P., Daniels, R., Costelloe, C. E., & Tonkin-Crine, S. (2024). Views and uses of sepsis digital alerts in National Health Service Trusts in England: Qualitative study with healthcare professionals. JMIR Human Factors, 11, e56949.
Maiano, L., Montuschi, A., Caserio, M., Ferri, E., Kieffer, F., Germanò, C., Baiocco, L., Celsi, L., Amerini, I., & Anagnostopoulos, A. (2023). A deep-learning-based antifraud system for car-insurance claims. Expert Systems with Applications, 231, 120644.
McEvoy, D., Sittig, D., Hickman, T. T., Aaron, S., Ai, A., Amato, M., Bauer, D., Fraser, G., Harper, J., Krall, M., Lehmann, C., Murphy, D., O’Kelley, B., Singh, H., Thomas, E., & Wright, A. (2016). Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. Journal of the American Medical Informatics Association, 24, 331–338.
McGreevey, J. D., Mallozzi, C. P., Perkins, R., Shelov, E., & Schreiber, R. (2020). Reducing alert burden in electronic health records: State of the art recommendations from four health systems. Applied Clinical Informatics, 11(1), 1–12.
Moreno, R., Buckelew, S. M., Accurso, E. C., & Raymond-Flesch, M. (2023). Disparities in access to eating disorders treatment for publicly insured youth and youth of color: A retrospective cohort study. Journal of Eating Disorders, 11(1).
Murphy, D., Meyer, A., Russo, E., Sittig, D., Wei, L., & Singh, H. (2016). The burden of inbox notifications in commercial electronic health records. JAMA Internal Medicine, 176(4), 559–560.
Nijor, S., Rallis, G., Lad, N., & Gokcen, E. (2022). Patient safety issues from information overload in electronic medical records. Journal of Patient Safety, 18.
Nurmawati, I., & Handayani, S. (2021). Returning of medical record documents among hospitalized patients: Literature review. Jurnal Aisyah: Jurnal Ilmu Kesehatan, 6(3).
Rogers, L., De Brún, A., & McAuliffe, E. (2020). Defining and assessing context in healthcare implementation studies: A systematic review. BMC Health Services Research, 20(1).
Russo, E., Singh, H., & Gregory, M. (2017). Electronic health record alert-related workload as a predictor of burnout in primary care providers. Applied Clinical Informatics, 8, 686–697.
Sane, M., Jantti, T., Marjamaa, A., Karvonen, J., & Raatikainen, P. (2024). A sane approach to the high volume of alert transmissions in remote monitoring: Turning off clinically non-relevant alerts decreases workload. European Heart Journal.
Scott, J., Finch, T., Bevan, M., Maniatopoulos, G., Gibbins, C., Yates, B., Kilimangalam, N., Sheerin, N., & Kanagasundaram, N. S. (2019). Acute kidney injury electronic alerts: Mixed methods evaluation of implementation into secondary care in England. BMJ Open, 9(12), e032925.
Sharifi, S., Zahiri, M., Dargahi, H., & Khiavi, F. F. (2021). Medical record documentation quality in the hospital accreditation. Journal of Education and Health Promotion, 10(1), 76.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104.
Stephens, A., Wynn, C. S., Hofstetter, A. M., Kolff, C. A., Peña, O., Kahn, E., Dasgupta, B., Natarajan, K., Vawdrey, D. K., Lane, M., Robbins‐Milne, L., Ramakrishnan, R., Holleran, S., & Stockwell, M. S. (2021). Effect of electronic health record reminders for routine immunizations and immunizations needed for chronic medical conditions. Applied Clinical Informatics, 12(5), 1101–1109.
Supriadi, S., & Dewi, N. (2020). Factors causing incomplete filling of medical records of inpatients in private hospitals X Tangerang City. Proceedings of the Third Andalas International Public Health Conference (AIPHC 2019), Padang, Indonesia.
Swari, S. J., Fransiska, C. K. R., Mudiono, D. R. P., Selviyanti, E., Alfiansyah, G., & Santi, M. W. (2023). Analysis of causes of inpatient medical records delayed retrieval at Muna Anggita Hospital Bojonegoro, Indonesia. Jurnal Medicoeticolegal dan Manajemen Rumah Sakit, 12(3), 324–340.
Vandenberk, B., & Raj, S. (2023). Remote patient monitoring: What have we learned and where are we going? Current Cardiovascular Risk Reports, 17, 103–115.
Wiji, L. R., Astuti, R., & Isworo, S. (2020). Completeness of filling medical record documents on inpatient ward, Ungaran General Hospital–Semarang, Central Java, Indonesia. GSC Biological and Pharmaceutical Sciences, 12(1), 145–155.
Willis, V. C., Craig, K. J. T., Jabbarpour, Y., Scheufele, E., Arriaga, Y., Ajinkya, M., Rhee, K., & Bazemore, A. (2022). Digital health interventions to enhance prevention in primary care: Scoping review. JMIR Medical Informatics, 10(1), e33518.
Wilson, F., Martin, M., Yamamoto, Y., Partridge, C., Moreira, E., Arora, T., Biswas, A., Feldman, H., Garg, A., Greenberg, J., Hinchcliff, M., Latham, S., Li, F., Lin, H., Mansour, S., Moledina, D., Palevsky, P., Parikh, C., Simonov, M., ... Ugwuowo, U. (2021). Electronic health record alerts for acute kidney injury: Multicenter, randomized clinical trial. BMJ, 372.
Zhang, X., Ma, C., Guo, F., & Li, Z. (2022). Does perceived overqualification cultivate angels or demons? Examining its interpersonal outcomes through pride from an evolutionary psychology perspective. Applied Psychology, 71(1), 243–270.
