Pengembangan Dashboard Business Intelligence untuk Monitoring Kelengkapan Rekam Medis Berbasis Indikator Audit
DOI:
https://doi.org/10.59024/jikas.v4i3.2177Keywords:
Analytical Dashboard, Business Intelligence, Completeness Of Medical Records, Health Information Management, Medical Record AuditAbstract
Medical record completeness is an important indicator of healthcare quality because it influences clinical documentation, continuity of care, hospital accreditation, and health insurance claim verification. However, audit results are commonly presented in static tabular reports, limiting timely monitoring and evidence-based decision-making. This study aimed to develop a Business Intelligence Dashboard for monitoring medical record completeness based on quantitative audit indicators. A descriptive quantitative design was employed using quantitative audit data obtained from 300 inpatient medical records. Data were processed through editing, coding, cleaning, tabulation, descriptive statistical analysis, and visualization using Microsoft Power BI Desktop. The Dashboard integrates Key Performance Indicators (KPI), completeness distribution, audit indicator performance, interactive filters, heat tables, and graphical summaries to support efficient monitoring. The audit results showed that 281 medical records (93.67%) were complete, while 19 records (6.33%) were incomplete, with an average completeness score of 93.06%. In addition, the Chi-Square test indicated no significant association between medical record completeness and health insurance claim status (p = 0.065). The developed Dashboard successfully transformed conventional audit reports into interactive visual information that facilitates rapid identification of documentation deficiencies, supports continuous monitoring, and enhances evidence-based decision-making for improving medical record quality in hospitals.
References
R. Rabiei, Y. M. Moghadam, N. Aslani, A. Garavand, A. Kazemnejad, and S. Abhari, “Requirements and challenges of hospital dashboards: A systematic literature review,” BMC Medical Informatics and Decision Making, vol. 22, no. 1, 2022, doi: 10.1186/s12911-022-02037-8. Link: https://doi.org/10.1186/s12911-022-02037-8
D. Helminski, J. B. Sussman, P. N. Pfeiffer, A. N. Kokaly, A. Ranusch, A. D. Renji, L. J. Damschroder, Z. Landis-Lewis, and J. E. Kurlander, “Dashboards in health care settings: Protocol for a scoping review,” JMIR Research Protocols, vol. 11, no. 3, e34894, 2022, doi: 10.2196/34894. Link: https://doi.org/10.2196/34894
S. Almasi, K. Bahaadinbeigy, H. Ahmadi, S. Sohrabei, and R. Rabiei, “Usability evaluation of dashboards: A systematic literature review of tools,” BioMed Research International, vol. 2023, Article ID 9990933, 2023, doi: 10.1155/2023/9990933. Link: https://doi.org/10.1155/2023/9990933
D. Helminski, J. B. Sussman, P. N. Pfeiffer, A. N. Kokaly, A. Ranusch, A. D. Renji, L. J. Damschroder, Z. Landis-Lewis, and J. E. Kurlander, “Development, implementation, and evaluation methods for dashboards in health care: Scoping review,” JMIR Medical Informatics, vol. 12, e59828, 2024, doi: 10.2196/59828. Link: https://doi.org/10.2196/59828
M. Malkani, E. Madan, D. Malkani, A. Madan, N. Singh, T. Bamji, and H. Sabharwal, “Rank ordered design attributes for health care dashboards including artificial intelligence: Usability study,” Online Journal of Public Health Informatics, vol. 16, 2024, doi: 10.2196/58277. Link: https://doi.org/10.2196/58277
E. Coiera, A. Chan, K. Brooke-Cowden, H. Rahimi-Ardabili, N. Halim, and C. Tufanaru, “Clinical and economic impact of digital dashboards on hospital inpatient care: A systematic review,” JAMIA Open, vol. 8, no. 4, ooaf078, 2025, doi: 10.1093/jamiaopen/ooaf078. Link: https://doi.org/10.1093/jamiaopen/ooaf078
H. J. van Elten, S. Sülz, E. M. van Raaij, and R. Wehrens, “Big data health care innovations: Performance dashboarding as a process of collective sensemaking,” Journal of Medical Internet Research, vol. 24, no. 2, e30201, 2022, doi: 10.2196/30201. Link: https://doi.org/10.2196/30201
A. W. Demsash, S. Y. Kassie, A. T. Dubale, A. A. Chereka, H. S. Ngusie, M. K. Hunde, M. D. Emanu, A. A. Shibabaw, and A. D. Walle, “Health professionals’ routine practice documentation and its associated factors in a resource-limited setting: A cross-sectional study,” BMJ Health & Care Informatics, vol. 30, no. 1, e100699, 2023, doi: 10.1136/bmjhci-2022-100699. Link: https://doi.org/10.1136/bmjhci-2022-100699
G. Bayisa, L. Gonfaa, K. Badasa, N. Dugasa, M. Abebe, H. Deressa, M. T. Regassa, A. Takele, and T. Tilahun, “Improving medical record completeness at Wallaga University Referral Hospital: A multidimensional quality improvement project,” BMJ Open Quality, vol. 13, no. 1, e002665, 2024, doi: 10.1136/bmjoq-2023-002665. Link: https://doi.org/10.1136/bmjoq-2023-002665
F. Wurster, M. Beckmann, N. Cecon-Stabel, K. Dittmer, T. J. Hansen, J. Jaschke, J. Köberlein-Neu, M.-R. Okumu, C. Rusniok, H. Pfaff, and U. Karbach, “The implementation of an electronic medical record in a German hospital and the change in completeness of documentation: Longitudinal document analysis,” JMIR Medical Informatics, vol. 12, e47761, 2024, doi: 10.2196/47761. Link: https://doi.org/10.2196/47761
F. Wurster, M. Beckmann, N. Cecon-Stabel, K. Dittmer, T. J. Hansen, J. Jaschke, J. Köberlein-Neu, M.-R. Okumu, C. Rusniok, H. Pfaff, and U. Karbach, “Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital: A longitudinal comparative document analysis,” BMC Medical Informatics and Decision Making, vol. 24, 2024, doi: 10.1186/s12911-024-02667-0. Link: https://doi.org/10.1186/s12911-024-02667-0
A. Shahbodaghi, H. Moghaddasi, F. Asadi, and A. Hosseini, “Documentation errors and deficiencies in medical records: A systematic review,” Journal of Health Management, vol. 26, no. 4, 2024, doi: 10.1177/09720634241229545. Link: https://doi.org/10.1177/09720634241229545
M. K. Hossain, J. Sutanto, P. W. Handayani, A. A. Haryanto, J. Bhowmik, and V. Frings-Hessami, “An exploratory study of electronic medical record implementation and recordkeeping culture: The case of hospitals in Indonesia,” BMC Health Services Research, vol. 25, no. 1, p. 249, 2025, doi: 10.1186/s12913-025-12399-0. Link: https://doi.org/10.1186/s12913-025-12399-0
S. P. Panda and A. Padhy, Business Intelligence with Power BI and Tableau: Cloud-Based Data Warehousing, Predictive Analytics, and Artificial Intelligence-Driven Decision Support. Deep Science Publishing, 2025, doi: 10.70593/978-93-7185-811-3. Link: https://doi.org/10.70593/978-93-7185-811-3
P. Sexton, K. Whiteman, E. L. George, M. Fanning, and K. Stephens, “Improving PACU throughput using an electronic dashboard: A quality improvement initiative,” Journal of PeriAnesthesia Nursing, vol. 37, no. 5, pp. 613–619, 2022, doi: 10.1016/j.jopan.2021.11.005. Link: https://doi.org/10.1016/j.jopan.2021.11.005
T. Y. A. Kenigsberg, A. M. Hause, M. M. McNeil, J. C. Nelson, A. Shoup, J. Goddard, K. Lou, K. E. Hanson, S. C. Glenn, and E. Weintraub, “Dashboard development for near real-time visualization of COVID-19 vaccine safety surveillance data in the Vaccine Safety Datalink,” Vaccine, vol. 40, no. 22, pp. 3064–3071, 2022, doi: 10.1016/j.vaccine.2022.04.010. Link: https://doi.org/10.1016/j.vaccine.2022.04.010
A. Field, Discovering Statistics Using IBM SPSS Statistics, 6th ed. London, U.K.: Sage Publications, 2024.
G. D. Ruxton and M. Neuhäuser, “When should Fisher's exact test be used instead of Chi-square?,” Methods in Ecology and Evolution, vol. 13, no. 6, pp. 1201–1208, 2022. doi: 10.1111/2041-210X.13819. Available: https://doi.org/10.1111/2041-210X.13819
Microsoft Corporation, Microsoft Power BI Documentation, 2025. Available: https://learn.microsoft.com/power-bi/
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