The history of modern health information management systems dates back to the 1920s when the American College of Surgeons (ACOS) established mechanisms to avail orderly patient records in clinical settings. That led to the formation of the American Health Information Management Association (AHIMA) in 1928, an association that dutifully organized and availed medical records for convenient access and sharing across multi-disciplinary teams. Ten years later, AHIMA became known as the American Association of Medical Record Librarians (AAMRL) to formalize the profession of medical record experts, alias “record librarians” (Brooks, 2015). Colleges then started training professionals to take up the newly created career, who later played a notable role in streamlining the profession.
Medical record keeping stayed in a gradual development trajectory until the birth and adoption of computers I the sixties and seventies. Large healthcare providers partnered with universities to digitize the records. However, that came at exorbitant prices, considering the high cost of optimizing software and availing personnel at the premises. As more significant advances were made in computing, the eighties and nineties saw rapid adoption of electronic solutions for record-keeping. The master patient index (MPI), a shared medical electronic database, is probably the greatest pre-millennial breakthrough (Jayatissa, 2018). Unlike the previous solutions that lacked mobility, MPI enabled data distribution across the hospital’s departments. That drove individual departments like radiology and laboratories to adopt specialized software that could still push key crucial data to the main MPI servers. It was not until the new millennium that computing was optimally deployed to mitigate simple human errors that were causing significant mortality.
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Notable pioneers
Four institutions and corporations played a pioneering role in the revolution of healthcare information. Eclipsys, a physician ordering system developed by the Lockheed Corporation, was deployed in California in 1971. Later, the University of Utah collaborated with 3M and Latter-Day Saints Hospital to develop Health Evaluation through Logical Processing (HELP), an automated clinical decision assistant. Another notable milestone was Massachusetts General Hospital and Harvard University’s joint project Computer Stored Ambulatory Record (COSTAR). In the following decade, at least fifteen other institutional and independent players had joined the efforts to digitize medical records (). That prompted the Institute of Medicine to set standards for what is now known as EHR (Electronic Healthcare Record) systems. The efforts were boosted by Tim Berners Lee’s invention of the world wide web in 1990, which enabled sharing of data across networks.
The structure and content of health information records have changed significantly. The problem-oriented medical record (POMR), the earliest computerized record system, was extremely rigorous. It included five facets that had to be hard-coded into the system – database, complete problem list, initial planning, daily progress note, and discharge summary (Sandy, 2019). In 1972, The Veterans Administration (VA) refined the POMR structure, leading to the novel Veterans Health Information System and Technology Architecture (VistA) which was more versatile. This offered physicians more capabilities; X-Rays, Lab Tests, Diets, nursing orders, Procedures, and medication ordering could be handled simultaneously. At the turn of the millennium, a computer-based patient record system (CPR) was developed. Unlike its predecessors, the CPR could handle automatic data backups, automatic log-offs, audit trails, and access control.
EHR Today
Ever since President George Bush called for the computerization of health records in his 2004 State of the Union Address, the EHR revolution has achieved a lot and has seen incredible reformation. That includes multiple reformations of the Health Insurance Portability and Accountability Act (HIPAA) to structure the systems’ mass adoption. With each iteration of reforms on the EHR systems, patient outcomes are noted to improve proportionally. A 2016 study by the University of Maryland School of Medicine established that electronic health record management systems improved significantly affected three attributes of patient outcome. These are; more accurate diagnoses, turnaround time, and improved mortality. On the other hand, physicians benefitted from the availability of critical information and better risk management, i.e., improved data aggregation analysis and communication. An increment of EHR adoption to 59% under the Health Information Technology for Economic and Clinical Health (HITECH) Act reduced average misdiagnoses by 3.5% in 2015 (Yanamadala et al., 2016). The study also observes a wide discrepancy in patient outcomes between facilities that upgraded from partial to full EHR implementation and those that did not. Yanamadala et al. was observed that it took patients 7.6% more time in facilities that had not upgraded, which can be significant in terms of cumulative hourly labor costs.
Projections for the future
While the current technological advancement in electronic health records is far from being substantially adopted, that should not be a barrier to yearning for even much more. And future projections would only be sensible if they promised to solve today’s EHR’s shortcoming. Glaser (2020) notes that the next generation digitized medical records must be plan-centric, which is to say they do not boggle the physician with data entry work but instead capitalize on creating value of the data. One way to achieve this is to mainstream health information exchange (HIE). The second recommendation is to deploy natural language processing (NLP) to automate data entry and retrieval, which appears to be taking much of the physician’s time – that is already sold out to research and administrative work. Lastly, advances in EHR systems should embrace big data and artificial intelligence in predicting possible clinical errors. While the prediction functionality is currently available, it is not robust enough. No wonder EHR records fail to detect a third of medication errors (Dollemore, 2020). Scaling down this ratio by incorporating AI would save more lives in the future.
References
Brooks, A. (2015). “Health Information Management History: Past, Present & Future.” Rasmussen University Health Sciences Blog . Retrieved https://www.rasmussen.edu/degrees/health-sciences/blog/health-information-management-history/
Dollemore, D. (2020). “Electronic Health Records Fail to Detect Up to 33% Of Medication Errors.” Utah University Healthcare Blog. Retrieved https://healthcare.utah.edu/publicaffairs/news/2020/05/electronic-health-records.php
Glaser, J. (2020). “It’s Time for a New Kind of Electronic Health Record.” Harvard Business Review. Retrieved https://hbr.org/2020/06/its-time-for-a-new-kind-of-electronic-health-record
Jayatissa, D. W. P., Dissanayake, P. V. H., & Hewapathirane, D. R. (2018). Review On Master Patient Index. Cornell University Computer Science Society . arXiv:1803.05994.
Sandy, B. (2019). “A History of EHR Through the Years.” ICA Notes. Retrieved https://www.icanotes.com/2019/04/16/a-history-of-ehr-through-the-years/
Yanamadala, S., Morrison, D., Curtin, C., McDonald, K., & Hernandez-Boussard, T. (2016). Electronic Health Records and Quality of Care: An Observational Study Modeling Impact On Mortality, Readmissions, And Complications. Medicine, 95(19).
