The Regenstrief Medical Record System: a quarter century experience

https://doi.org/10.1016/S1386-5056(99)00009-XGet rights and content

Abstract

Entrusted with the records for more than 1.5 million patients, the Regenstrief Medical Record System (RMRS) has evolved into a fast and comprehensive data repository used extensively at three hospitals on the Indiana University Medical Center campus and more than 30 Indianapolis clinics. The RMRS routinely captures laboratory results, narrative reports, orders, medications. radiology reports, registration information, nursing assessments, vital signs, EKGs and other clinical data. In this paper, we describe the RMRS data model, file structures and architecture, as well as recent necessary changes to these as we coordinate a collaborative effort among all major Indianapolis hospital systems, improving patient care by capturing city-wide laboratory and encounter data. We believe that our success represents persistent efforts to build interfaces directly to multiple independent instruments and other data collection systems, using medical standards such as HL7, LOINC, and DICOM. Inpatient and outpatient order entry systems, instruments for visit notes and on-line questionnaires that replace hardcopy forms, and intelligent use of coded data entry supplement the RMRS. Physicians happily enter orders, problems, allergies, visit notes, and discharge summaries into our locally developed Gopher order entry system, as we provide them with convenient output forms, choice lists, defaults, templates, reminders, drug interaction information, charge information, and on-line articles and textbooks. To prepare for the future, we have begun wrapping our system in Web browser technology, testing voice dictation and understanding, and employing wireless technology.

Section snippets

Background/history

We began our efforts to create a computer-stored medical record in 1972, with Dr Charles Clark and his 35 diabetes patients, at what was then called Marion County General Hospital. We thought that we could capture all patient data on this small number of patients through manual methods, and that it would take about a year to complete the medical record. Then we could get on with the ‘fun’ part of this effort—automated diagnoses and management. We built programs to enter patient data, to store

Overview and methods

The Regenstrief Medical Record System (RMRS) is large, fast, comprehensive, long term and introspective [8]. It contains more than 200 million separate coded observations, 3.25 million narrative reports, 15 million prescriptions and 212,000 electrocardiographic (EKG) tracings. It carries data for more than a 1.3 million patients, and it can display the records for any one of these patients in less than a second. It is used by more than 1300 medical center nurses, 1000 physicians and 220 medical

Data capture—the difficult side of medical record systems

Any demonstration system, pre-loaded by hand with clinical data, will illustrate the great benefits of EMRs—i.e. visit notes, flowsheets, graphs and statistics. However, demonstration systems do not convey the effort needed to capture such data from processes within a live institution. Though the increasing availability of HL7 interfaces and universal codes for clinical variables (LOINC) [18] and other clinical concepts (SNOMED [20], Read [22] codes) has made it easier to capture clinical data,

Clinical outputs for patient care

The computer record provides a number of ‘by patient’ reports that can be obtained as soft (video terminal displays) or hard copies to serve care providers. Hard copy continues to be popular in the clinic because it is easy to produce paper reports automatically at check-in.

Three reports are typically produced for each clinic visit: the encounter form described above, a flowsheet of all of the structured medical record data, and a specialty snap shot. This latter report is designed to provide

Reminders and informational feedback to providers

The computer provides informational feedback at many points in the order writing process [24]. When physicians enter a problem into the workstation, the computer tailors the order menus to that problem (see Fig. 15 for the treatment order menu for peptic ulcer disease). Physicians can generate an order simply by choosing one of the pre-formed options after entering the patient’s problem. When the user enters an order, the computer pops up an information window that reports the order’s price,

Search and retrieval capabilities and retrieval capabilities—cross-patient reports

Users with appropriate privileges can perform cross-patient searches for IRB-approved research and quality management purposes [35]. They can use the CARE language [7] to search the entire data base, a subset of the data base, or several institutions’ databases for patients whose EMR contains particular patterns of data. This same system can be used to implement reminder rules or queries.

Fast retrieval is a second way to search the data base. It uses direct indexes by clinical variable (e.g.

Conclusion and future challenges

Since we began in 1973, physicians have always been happy with the retrieval and display aspects of our medical record system. Having immediate access to all diagnostic reports, operative notes, discharge summaries, drug records and a large portion of other notes is a joy compared to the slow and erratic alternatives of requesting the hard copy chart or calling each diagnostic service for the results. The happy reaction we have received from physicians and other health professionals about our

Acknowledgements

This work was performed at the Regenstrief Institute for Health Care, Indianapolis, IN, and was supported in part by the Agency for Health Care Policy and Research (Grant HS 07719) and the National Library of Medicine (Contracts N01-LM-4-3410 and N01-LM-6-3546).

References (40)

  • McDonald CJ. Computer applications to ambulatory care. Proceedings of the IEEE Conference on Systems, Man, and...
  • McDonald CJ, Bhargava B. Tree systems for medical information processing. Proceedings of the Eleventh Annual Rocky...
  • McDonald CJ, Martin G. A model lab information system. Proceedings of the Fifth Annual Pittsburgh Conference on...
  • Carlstedt B, Jeris DW, Kramer W, Griefenhage R, McDonald CJ. A computer-based pharmacy system for ambulatory patient...
  • McDonald CJ, Wiederhold G, Simborg DW. A discussion of the draft proposal for data exchange standards for clinical...
  • McDonald CJ, Bhargava B, Jeris DW. A clinical information system (CIS) for ambulatory care. Proceedings of the AFIPS...
  • McDonald CJ. Action-Oriented Decisions in Ambulatory Medicine. Yearbook Medical Publishers, Chicago,...
  • McDonald CJ, Hui SL, Smith DM, Tierney WM, Cohen SJ, Weinberger M. Reminders to physicians from an introspective...
  • Committee on Improving the Patient Record. The computer-based patient record. Dick RS, Steen EB, Editors. Institute of...
  • McDonald CJ, Blevins L, Tierney WM, Martin DK. The Regenstrief Medical Records. MD Comput 1988;...
  • Overhage JM, Tierney WM, McDonald CJ. Design and implementation of the Indianapolis network for patient care and...
  • McDonald CJ, Tierney WM. The medical gopher-A micro computer system to help find, organize and decide about patient...
  • Zafar T, Overhage JM, McDonald CJ. Continuous speech recognition for clinicians. JAMIA (under...
  • McDonald CJ, Dexter PR, Overhage JM, Takesue B. Health Informatics Standards: A View From Mid-America. IMIA Yearbook of...
  • Health Level Seven. An application protocol for electronic data exchange in healthcare environments, Version 2.3....
  • Digital Imaging and Communications in Medicine (DICOM). the ACR-NEMA DICOM Standard, publication PS3.1-PS3.12. Rosslyn,...
  • NCPDP Telecommunication Standard Format. Version 3.2 Phoenix, AZ: National Council for Prescription Drug Programs,...
  • Forrey AW, McDonald CJ, DeMoor G, Huff SM, Leavelle D, Leland D, Fiers T, Charles L, Griffm B, Stalling F, Tullis A,...
  • McCray AT, Razi AM, Bangalore AK, Browne AC, Stavri PZ. The UMLS Knowledge Source Server: a versatile Internet-based...
  • American College of Pathology. SNOMED: Systematized Nomenclature of Medicine. 2nd ed. v 1 and 2. Skokie, IL: American...
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