Atif Zafar

The Regenstrief Medical Record System: a quarter century experience

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.

A Framework for evaluating the costs, effort, and value of nationwide health information exchange

OBJECTIVE The nationwide health information network (NHIN) has been proposed to securely link community and state health information exchange (HIE) entities to create a national, interoperable network for sharing healthcare data in the USA. This paper describes a framework for evaluating the costs, effort, and value of nationwide data exchange as the NHIN moves toward a production state. The paper further presents the results of an initial assessment of the framework by those engaged in HIE activities. DESIGN Using a literature review and knowledge gained from active NHIN technology and policy development, the authors constructed a framework for evaluating the costs, effort, and value of data exchange between an HIE entity and the NHIN. MEASUREMENT An online survey was used to assess the perceived usefulness of the metrics in the framework among HIE professionals and researchers. RESULTS The framework is organized into five broad categories: implementation; technology; policy; data; and value. Each category enumerates a variety of measures and measure types. Survey respondents generally indicated the framework contained useful measures for current and future use in HIE and NHIN evaluation. Answers varied slightly based on a respondents participation in active development of NHIN components. CONCLUSION The proposed framework supports efforts to measure the costs, effort, and value associated with nationwide data exchange. Collecting longitudinal data along the NHINs path to production should help with the development of an evidence base that will drive adoption, create value, and stimulate further investment in nationwide data exchange.

Field test results of a new ambulatory care Medication Error and Adverse Drug Event Reporting System--MEADERS.

PURPOSE In this study, we developed and field tested the Medication Error and Adverse Drug Event Reporting System (MEADERS)—an easy-to-use, Web-based reporting system designed for busy office practices. METHODS We conducted a 10-week field test of MEADERS in which 220 physicians and office staff from 24 practices reported medication errors and adverse drug events they observed during usual clinical care. The main outcomes were (1) use and acceptability of MEADERS measured with a postreporting survey and interviews with office managers and lead physicians, and (2) distributions of characteristics of the medication event reports. RESULTS A total of 507 anonymous event reports were submitted. The mean reporting time was 4.3 minutes. Of these reports, 357 (70%) included medication errors only, 138 (27%) involved adverse drug events only, and 12 (2.4%) included both. Medication errors were roughly equally divided among ordering medications, implementing prescription orders, errors by patients receiving the medications, and documentation errors. The most frequent contributors to the medication errors and adverse drug events were communication problems (41%) and knowledge deficits (22%). Eight (1.6%) of the reported events led to hospitalization. Reporting raised staff and physician awareness of the kinds of errors that occur in office medication management; however, 36% agreed or strongly agreed that the event reporting “has increased the fear of repercussion in the practice.” Time pressure was the main barrier to reporting. CONCLUSIONS It is feasible for primary care clinicians and office staff to report medication errors and adverse drug events to a Web-based reporting system. Time pressures and a punitive culture are barriers to event reporting that must be overcome. Further testing of MEADERS as a quality improvement tool is warranted.

A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.

OBJECTIVES To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes. METHODS We integrated the Dragon NaturallySpeaking v4.0 speech recognition engine into the Regenstrief Medical Record System. We captured the text output of the speech engine prior to error correction by the speaker. We also acquired a set of human transcribed but uncorrected notes for comparison. We then attempted to error correct these notes based on looking at the context alone. Initially, three domain experts independently examined 104 ASR notes (containing 29,144 words) generated by a single speaker and 44 human transcribed notes (containing 14,199 words) generated by multiple speakers for errors. Collaborative group sessions were subsequently held where error categorizes were determined and rules developed and incrementally refined for systematically examining the notes and classifying errors. RESULTS We found that the errors could be classified into nine categories: (1) announciation errors occurring due to speaker mispronounciation, (2) dictionary errors resulting from missing terms, (3) suffix errors caused by misrecognition of appropriate tenses of a word, (4) added words, (5) deleted words, (6) homonym errors resulting from substitution of a phonetically identical word, (7) spelling errors, (8) nonsense errors, words/phrases whose meaning could not be appreciated by examining just the context, and (9) critical errors, words/phrases where a reader of a note could potentially misunderstand the concept that was related by the speaker. CONCLUSIONS A simple method is presented for examining errors in transcribed documents and classifying these errors into meaningful and useful categories. Such a classification can potentially help pinpoint sources of such errors so that measures (such as better training of the speaker and improved dictionary and language modeling) can be taken to optimize the error rates.

Information Technology in PBRNs: The Indiana University Medical Group Research Network (IUMG ResNet) Experience

Objective: Research in practice-based research networks (PBRNs) is hampered by difficulty managing, identifying, and enrolling potential subjects. Well-designed informatics applications can greatly improve these processes. Methods: We considered a literature review, discussion with PBRN researchers, and personal experience to outline important principles to apply when considering electronic data collection in a PBRN. We provide specific working examples of electronic means we use to improve data collection and patient enrollment. Results: Our PBRN has screened more than 18,000 patients and enrolled more than 6000 study subjects in 5 years. Less than 2% of potentially eligible patients are missed by our research assistants. We achieved this high rate of success through extensive integration of the ResNet infrastructure (research databases and personnel) with an electronic medical record (EMR) system and computerized provider order entry. We make extensive use of widely used standards for data storage, definition, and transmission to ensure data reusability. We successfully implemented a real-time means to identify follow-up patients. Conclusion: Electronic data collection can greatly facilitate PBRN research, particularly by improving data management and identification of eligible patients. Key principles to ensure successful implementation include use of data standards and centralized electronic data management.