Automating Compliance in Healthcare: Tools and Techniques You Need
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I3P105Keywords:
Compliance Automation, Healthcare Compliance, HIPAA, GDPR, HITRUST, AI in Compliance, Machine Learning, Healthcare RegulationsAbstract
Automating compliance in healthcare is increasingly crucial as regulatory demands become more stringent and complex. Healthcare providers are required to ensure patient data privacy, maintain accurate records, and adhere to constantly evolving legal standards. Manual methods of managing compliance are time-consuming, prone to human error, and inefficient. Automation offers a solution by streamlining the process, reducing risks, and ensuring real-time adherence to regulations like HIPAA. By integrating tools such as AI-powered compliance monitoring, robotic process automation (RPA), and advanced data analytics, healthcare organizations can track, report, and manage compliance more effectively. Techniques such as automated audits, real-time reporting dashboards, and security information and event management (SIEM) systems help flag potential compliance breaches before they occur. These tools also facilitate compliance documentation, making audits easier and more transparent. Moreover, automation enhances the ability to respond swiftly to regulatory changes, helping organizations stay ahead of compliance updates without the need for manual interventions. This approach not only ensures that healthcare institutions remain compliant but also frees up valuable time and resources that can be redirected toward improving patient care. As healthcare organizations continue to adopt new technologies like cloud computing and electronic health records (EHR), automating compliance will be pivotal to maintaining data integrity, patient safety, and operational efficiency. Embracing automation not only mitigates risks but also fosters a culture of continuous compliance, essential in today’s fast-evolving healthcare landscape
Downloads
References
[1] Bond, W. S., & Hussar, D. A. (1991). Detection methods and strategies for improving medication compliance. American journal of hospital pharmacy, 48(9), 1978-1988.
[2] Lobach, D. F., & Hammond, W. E. (1997). Computerized decision support based on a clinical practice guideline improves compliance with care standards. The American journal of medicine, 102(1), 89-98.
[3] Lacity, M. C., & Willcocks, L. P. (2016). A new approach to automating services. MIT Sloan Management Review, 58(1), 41-49.
[4] Turetken, O., Elgammal, A., van den Heuvel, W. J., & Papazoglou, M. P. (2012). Capturing compliance requirements: A pattern-based approach. IEEE software, 29(3), 28-36.
[5] Waring, J., Lindvall, C., & Umeton, R. (2020). Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial intelligence in medicine, 104, 101822.
[6] Meystre, S. M., Friedlin, F. J., South, B. R., Shen, S., & Samore, M. H. (2010). Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC medical research methodology, 10, 1-16.
[7] Neamatullah, I., Douglass, M. M., Lehman, L. W. H., Reisner, A., Villarroel, M., Long, W. J., ... & Clifford, G. D. (2008). Automated de-identification of free-text medical records. BMC medical informatics and decision making, 8, 1-17.
[8] Lin, H. S., & Stead, W. W. (Eds.). (2009). Computational technology for effective health care: immediate steps and strategic directions.
[9] Bender, D., & Sartipi, K. (2013, June). HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. In Proceedings of the 26th IEEE international symposium on computer-based medical systems (pp. 326-331). IEEE.
[10] Andrade, S. E., Kahler, K. H., Frech, F., & Chan, K. A. (2006). Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiology and drug safety, 15(8), 565-574.
[11] Amarasingham, R., Plantinga, L., Diener-West, M., Gaskin, D. J., & Powe, N. R. (2009). Clinical information technologies and inpatient outcomes: a multiple hospital study. Archives of internal medicine, 169(2), 108-114.
[12] Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
[13] Choo, P. W., Rand, C. S., Inui, T. S., Lee, M. L. T., Cain, E., Cordeiro-Breault, M., ... & Platt, R. (1999). Validation of patient reports, automated pharmacy records, and pill counts with electronic monitoring of adherence to antihypertensive therapy. Medical care, 37(9), 846-857.
[14] Kuperman, G. J., & Gibson, R. F. (2003). Computer physician order entry: benefits, costs, and issues. Annals of internal medicine, 139(1), 31-39.
[15] Murff, H. J., FitzHenry, F., Matheny, M. E., Gentry, N., Kotter, K. L., Crimin, K., ... & Speroff, T. (2011). Automated identification of postoperative complications within an electronic medical record using natural language processing. Jama, 306(8), 848-855.
