Regenstrief Medical Record System (RMRS)

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The Regenstrief Medical Record System (RMRS) is a comprehensive homegrown data model for electronic health records (EHR) developed by the Regenstrief Institute of Indiana University[1][2][3], as well as an associated database-management system (DBMS), communications system, and a suite of clinician-facing client-side applications called the Medical Gopher[4][5][1]. The system pioneered Indiana Health Information Exchange—one of the first health-information exchanges[2]—as well as one of the first computer-assisted order-entry workstations[4][2].

Between 1972[5][1][2] and 2016[3], RMRS stored and managed patient records across multiple institutions of the greater Indianapolis area; at its peak, it contained more than 200 million coded observations and 3.25 million narrative reports, for more than 1.5 million patients, across at least fifteen independent healthcare organizations[1]. However, its usage was largely retired in 2016[3][2]. It is succeeded today by the Indiana Network for Patient Care (INPC), the Indiana Health Information Exchange, the Regenstrief Teaching EMR, and Arden syntax—all of which were originally based upon the RMRS data model.

Nascence: 1972–1990

1972 was a year from the age of the 16-bit minicomputer[6]. That year, Clement McDonald and Charles Clark, two physicians at Sidney & Lois Eskenazi Hospital (then called Marion County General Hospital[1][7]) used a PDP-11 minicomputer[1][6] to create a computer-stored medical record for 35 patients with diabetes, with the intent of automating diagnosis and management after manually capturing patient data, using a hardcoded but generic data structure[8][9][1]. Their goals included the following[10]:

  1. Eliminating logistic problems of paper records, such as availability and indecipherability.
  2. Reducing the work of clinical bookkeeping that is incidental and nonessential to patient management: “no more missed diagnoses when laboratory evidence shouts its existence, no more forgetting about required preventive care”.
  3. Making informational “gold” in medical records available to clinical, epidemiological, and managerial research.

After encountering difficulty with capturing comprehensive data for the pilot population of 35 patients at their diabetes clinic, McDonald, Clark, and other colleagues would build several systems for capturing data at laboratory and pharmaceutical sources[11][12][1].

Data model and RDBMS

Over 1972–1974, it was found that maintaining a hard-coded data model was not sustainable, due to having to change multiple programs whenever any change was made to their data model’s file structure; the decision was therefore made to create a relational-database management system (RDBMS), due to no commercial DBMS being available for the PDP-11 microcomputer being used[13].

RDBMS, by 1989, ran on several VAX superminicomputer workstations[14] and stored the RMRS relational data model in several large files. Records were physically sorted by patient ID, observation ID, and date/time (reverse chronologically). A single term dictionary controlled the system’s concept vocabulary, including all diagnoses, findings, variables (e.g., Glasgow coma score, diastolic blood pressure), and sets of terms. Questionnaires were defined as structured lists of questions and variables stored in the RMRS; these questionnaires were flexible and accepted codes from many different code systems (e.g., ICD-9, CPT-4, local Regenstrief codes) that were associated with each variable’s term. The physical RDBMS and its data model resembled the following:[15][5][1]

  • Dictionary/master files containing:
    • Terms, each with a name, synonyms, external references (e.g., ICD-9 and SNOMED), etc.
    • Structured-observation types, e.g., “microbiology”.
    • Numeric-observation types, each with a scale factor, reference ranges, etc.
    • Coded-observation types, each with ordinals, nominals, etc.
    • Doctors, each with a name, a license, a speciality, etc.
    • Relationships between these relations:
      • Structured-observation types, numeric-observation types, and coded-observation types were subclasses of terms.
      • Each term could be an aggregate of at least two structured-observation types as its “elements”.
      • Each term could have any number of coded-observation types as its value set.
      • Each term could have one numeric-observation type as its unit.
  • Operational data containing:
    • Encounters, each with a date, billing data, etc.
    • Orders, each with a date/time, a priority, notes, etc.
    • Patients, each with a name, address, etc.
    • Medical-record items, each with a date/time, etc.
    • Narrative reports, each with a header, text, etc.
    • Numeric observations, each with a value, etc.
    • Binary data, e.g., images, voice recordings, electrocardiograms, etc.
    • Coded observations.
    • Relationships between these relations:
      • Each encounter was associated with one patient.
      • Each order was associated with one encounter.
      • Each medical-record item was the result of one order.
      • Each medical-record item was associated with one patient.
      • Each medical-record item had one term as its parameter.
      • Narrative reports, numeric observations, binary data, and coded observations were subclasses of medical-record items.
      • Each coded observation could have any number of terms for its value(s).

Data capture, research, and display

By 1984, comprehensive multisite data capture by RMRS had improved, and new data standards were being developed to reduce the necessity of ancillary service systems[16][5][1]. And even as early as the 1980s, several retrospective clinical studies would be performed using the RMRS dataset[17][18][19][20].

In addition, computerized physician order entry (CPOE) in the outpatient setting was initiated in 1984, which was later extended to the inpatient setting in 1990. These CPOE systems would evolve into elaborate “Medical Gopher” client-side workstations.[21][5][1] Although RMRS’s developers noted initial physician unhappiness in reaction to computerized order entry, this unhappiness would later improve as the Gopher system would improve[1].

Even at these early stages, RMRS generated much research about clinical decision support in the outpatient setting, especially regarding rule-based computer reminders[22][23][24]. Furthermore, in the 1980s, RMRS’s reminder-rule systems and Health Evaluation through Logical Processing (HELP) would give rise in 1989 to Arden syntax, a markup language for representing shared medical knowledge, which would later become an HL7 standard in 1999 and an American National Standards Institute (ANSI) standard in 2002.[25].

Maturity: 1990–2000

By the 1990s, RMRS continued to grow and RMRS was gradually expanded from the outpatient clinics to inpatient settings. It would facilitate the care of more than one million patients while generating dozens of studies. At its peak circa 1999, it contained more than 200 million coded observations, 3.25 million narrative reports, 15 million prescriptions, and 212,000 electrocardiograms, for more than 1.5 million patients—across at least fifteen independent healthcare organizations, with access by more than 2500 employees.[5][1]

Core system: three-legged stool

RMRS’s flagship institutions continued to be Sidney & Lois Eskenazi Hospital (which by this period had been renamed from Marion County General Hospital to Wishard Hospital)[7], as well as the three hospitals of Indiana University Health (formerly Clarian Health)[26]—together with Regenstrief, these together were characterized as RMRS’s “three-legged stool”[5].

Its homegrown central relational-database management system (RDBMS) ran on DEC Alpha servers with an ancillary Novell personal-microcomputer network, both hosted by Eskenazi Hospital.[1] By 1999, RMRS also managed the medical records of all outreach/homeless clinics in the Indianapolis area, as well as mental-health clinics. These would connect with the central Eskenazi hub system, via ethernet cables, Transmission System 1 (T1) lines, and dedicated telephone lines, largely with HL7 messages over Internet Protocol (IP).[1]

In this core system, RMRS captured clinical data from more than two dozen disparate systems, such as direct capture from bedside measuring devices, direct links to electrocardiography carts, direct links to financial systems, regular technician entry from public health records, nurse radio/computerized dictation and voice recording, clerk-entered questionnaire/form results, and computerized physician entry via “Medical Gopher” client-side microcomputer workstations.[21][5][1] Data standards used as of 1999 included HL7 messages, DICOM, NCPDP, LOINC, ICD-9, CPT-4, and ACR radiology codes; UMLS and SNOMED were being explored but not supported at this time[5][1].

Physician order entry and clinical-data charts were initially displayed in text-based interfaces from “Medical Gopher” computer terminals. Eventually, graphical interfaces in web pages were also supported; hard copies were also printed on paper—in general, these were designed to yield specialty-specific, single-page, information-dense overviews of each patient, akin to hospital “scut” cards.[5][1]

Informational feedback such as alerts, e.g., for contraindicated medications, occurred during order entry; research was extended from the outpatient setting[27] to the inpatient setting during the 1990s[28][29][30][31]. Order suggestions were also given depending on rules expressed in a pair of the homegrown rule languages, CARE and G-CARE; these included including “corollary orders”, as well as formulary restrictions from several health-maintenance organizations (HMOs)[32][31][5][1]; for example, entering DVT in a problem list would trigger order sets for heparin and warfarin[1][33]. Physicians, however, were “never blocked from completing an order” but were rather made “aware of the potential problems that an order may cause” [1].

Statewide health-information exchange

Outside of the four-hospital core system, the RMRS data model was further developed during this period to span multiple healthcare systems: separate medical repositories (data “vaults”) were created for each healthcare systems, but these vaults would share a single standardized term dictionary and a “global patient index”. A personal-name matching algorithm would determine which patient records in different healthcare systems referred to the same patient, assigning them the same “global patient ID”. This “cross-enterprise” normalization occurred at a low level in the data model, and “virtual medical records” from multiple healthcare systems were able to be automatically displayed to clinicians with minimal modification to client applications.[34][35][5][1]

By 1996, the now-“cross-enterprise” RMRS had evolve into one of the first statewide health-information exchanges (HIE): the Indiana Network for Patient Care and Research (INPCR) database[34][1]. This would later evolve during the late 1990s into the Indiana Network for Patient Care (INPC) database.

Twilight: 2000–2016

A 1999 review of RMRS characterized its clinician users across its several core system as “happy”; it identified several challenges for the future, including further accommodating dictation-based workflows, further developing its then-new web-based graphical user interface, exploring wireless and remote clinician access, and maintaining secure authentication with biometric technology[1].

However, by 2014, the three hospitals of Indiana University Health (formerly Clarian Health)[26] had switched from to Cerner’s EHR suite[36], and Sidney & Lois Eskenazi Hospital remained the only major healthcare institution directly using RMRS and its Gopher system for order entry.

In the meantime, the Regenstrief Institute in 2004 gave control of INPC to a new nonprofit corporation, the Indiana Health Information Exchange (IHIE), which was formed from an alliance between thirteen Indiana healthcare institutions and incorporated in 2004[37], with the goal to create sustainable business models and provide commercial-level support for INPC, while the Regenstrief Institute would continue managing research access to its information[38].

RMRS’s Gopher system continued to serve Sidney & Lois Eskenazi Hospital’s patients until 2016, when Eskenazi Hospital switched from RMRS to Epic[3][2]. This was a part of a greater trend of the replacement of homegrown EHRs in the United States by vended products that were government-certified under meaningful-use programs[2].


As of 2021, the Indiana Health Information Exchange (IHIE) and the Regenstrief continue to manage INPC and provide statewide health-information exchange via several client-side applications[39][38], which were originally derived from RMRS’s homegrown data model from the 1970s, and which and continue to use the cross-enterprise global patient index that RMRS pioneered in the 1990s[34].

Regenstrief, the Indiana School of Medicine, Sidney & Lois Eskenazi Hospital, and the American Medical Association jointly created the Regenstrief Teaching EMR (tEMR) as a “copy” of the RMRS data model and other technology. The Regenstrief tEMR has been successfully deployed in at twelve health-professional schools with over 11,000 unique student users as of 2021.[40]

RMRS’s clinical decision support in the 1980s, along with Health Evaluation through Logical Processing (HELP), would give rise in 1989 to Arden syntax, a markup language for representing shared medical knowledge. These continue on as HL7 and American National Standards Institute (ANSI) standards.[25].


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Submitted by J. S. Choi, MD.