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The impact of a computerized medical record summary system on incidence and length of hospitalization.

The present study examined the influence of a computerized medical record summary system on incidence and length of hospitalization. Additional indicators of process of care were also considered. A prospective, randomized design was used where 241 experimental patients had a computerized medical record summary and 238 control patients had the traditional medical record. All patients were followed and compared over a two-year period. Although no difference in incidence of hospitalization was found between patients with and without the summary, during the second year, patients with summaries spent fewer days in the hospital. Patients with summaries also had more completed referrals for consultation, assigned diets, detected new problems and diagnostic tests on record than patients without the summary.

Chicago↗

[Introducing computer units into the reception office as part of the Vrapce Psychiatric Hospital Information System].

Computerized medical record has become a necessity today, because of both the amount of present-day medical data and the need of better handling and processing them. In more than 120 years of the Vrapce Psychiatric Hospital existence, the most important changes in the working concept of the reception office took place when computer technology was introduced into the routine use. The reception office of the Hospital is the vital place where administrative activities intersect with medical care for a patient presenting to the Hospital. The importance of this segment of the Hospital is emphasized by the fact that the reception office is in function and at patients' disposition round-the-clock, for 365 days a year, with great frequency of patients. The shift from the established way of registering medical data on patient admission in handwriting or, later, typescript, to computer recording was a challenging and demanding task (from the aspects of hardware, software, network, education) for the development team as well as for the physicians because it has changed the concept (logic of the working process) of previous way of collecting the data from the patient (history, status, diagnostic procedures, therapy, etc.). The success in the development and implementation of this project and the confirmation of its usefulness during the four-year practice at Vrapce Psychiatric Hospital are best illustrated by the fact that other psychiatric hospitals in Croatia have already introduced or are introducing it in their daily practice.

Croatia↗

A computerized medical record. "The Balsfjord system".

The "Balsfjord system" is a fully computerized system of medical records. The system is developed by the University of Tromsö in cooperation with the district physicians in Balsfjord. It is designed for Norwegian general practice based on microcomputers. Each general practitioner has a computer terminal in his/her consulting room, and all the terminals can operate on the same file simultaneously. The main archive is a hard disc, in addition there is a a secondary diskette archive for old, out of date record versions. Each medical record is composed of seven components, where the medical information is logically organized. The system is able to print out prescriptions, sick-leave forms, and parts or the whole record. Included in the program are two statistical systems. One is based on data from encounters, and the other is a search and statistical system based on the individual record as the unit. The system has been very successful for the two and a half years it has been in use. Both the doctors and the ancilliary staff have appreciated the system in their daily work.

Computers↗

Using medication list--problem list mismatches as markers of potential error.

The goal of this project was to specify and develop an algorithm that will check for drug and problem list mismatches in an electronic medical record (EMR). The algorithm is based on the premise that a patient's problem list and medication list should agree, and a mismatch may indicate medication error. Successful development of this algorithm could mean detection of some errors, such as medication orders entered into a wrong patient record, or drug therapy omissions, that are not otherwise detected via automated means. Additionally, mismatches may identify opportunities to improve problem list integrity. To assess the concept's feasibility, this study compared medications listed in a pharmacy information system with findings in an online nursing adult admission assessment, serving as a proxy for the problem list. Where drug and problem list mismatches were discovered, examination of the patient record confirmed the mismatch, and identified any potential causes. Evaluation of the algorithm in diabetes treatment indicates that it successfully detects both potential medication error and opportunities to improve problem list completeness. This algorithm, once fully developed and deployed, could prove a valuable way to improve the patient problem list, and could decrease the risk of medication error.

Algorithms↗

Empirical derivation of an electronic clinically useful problem statement system.

Problem lists are tools to improve patient management. In the medical record, they connect diagnoses to therapy, prognosis, and psychosocial issues. Computer-based problem lists enhance paper-based approaches by enabling cost-containment and quality assurance applications, but they require clinically expressive controlled vocabularies. Because existing controlled vocabularies do not represent problem statements at a clinically useful level, we derived a new canonical problem statement vocabulary through semi-automated analysis and distillation of provider-entered problem lists collected over 6 years from 74,696 patients. We combined automated and manual methods to condense 891,770 problem statements entered by 1961 care providers at Grady Memorial Hospital in Atlanta, Georgia, to 15,534 Canonical Clinical Problem Statement System (CCPSS) terms. The nature and frequency of problem statements were characterized, interrelations among them were enumerated, and a database capturing the epidemiology of problems was created. The authors identified 23,503 problem relations (co-occurrences, sign-symptom complexes, and differential diagnoses) and 22,690 modifier words that further categorized "canonical" problems. To assess completeness, CCPSS content was compared with that of the 1997 Unified Medical Language System Metathesaurus (containing terms from 44 clinical vocabularies). Unified Medical Language System terms expressed 25% of individual CCPSS terms exactly (71% of problems by frequency), 27% partially, and 48% poorly or not at all. Clinicians judged that CCPSS terms completely captured their clinical intent for 84% of 686 randomly selected free-text problem statements. The CCPSS represents clinical concepts at a level exceeding that of previous approaches. A similar national approach could create a standardized, useful, shared resource for clinical practice.

Humans↗

Extracting medical knowledge for a coded problem list vocabulary from the UMLS Knowledge Sources.

INTRODUCTION: The Unified Medical Language System (UMLS) Knowledge Sources embody a rich source of medical knowledge. We sought to extract a portion of this knowledge by incorporating information about relationships between UMLS concepts into an existing problem list vocabulary. METHODS: We matched terms from the coded problem list of The Medical Record (TMR), a computer-based patient record system, with those found in the UMLS Metathesaurus. Those UMLS concepts that participate in 'parent' relationships with the matched TMR concepts were translated back into TMR codes and the relationship information was retained for integration into the coded problem list of TMR. RESULTS: Of the coded problems currently in use in TMR, 67% (1627/2436) could be matched by normalized string matches to the UMLS Knowledge Sources. Of these matched TMR concepts, 91% (1488/1627) participated in at least one UMLS-identified parent relationship but only 28% of the matched concepts (454/1627) participated in parent relationships that already matched to a TMR code. As a result, although 67% of TMR codes were matched to UMLS concepts, only 19% of our original problem list (454/2436) could be augmented by relationship information contained in UMLS without improving the rate of matches or adding additional UMLS concepts as coded problems in TMR. CONCLUSION: This study illustrates the rapid decline in overall rates of matching that result from a multiplicative effect of successive matches of terms to concepts, concepts to relationships and concepts back to entry terms. This effect will hamper any effort to extract relationship knowledge from the UMLS for incorporation into an entry vocabulary that is not already one of the source vocabularies of the UMLS Metathesaurus.

Humans↗

[Electronic organization of patient records as a component of integrated medical informatics].

Conventional patient records are not only inconvenient to store, their structure is often not consistent and faulty, research is difficult and quality control is not possible. One solution to this problem is the computerized patient record. It contains all necessary data beginning with the first medical statements and ending with the final results of treatment. At any time and from any with the system connected computer terminal it allows immediate access to all medical records. In addition to medical information also administrative data are stored. The here presented system of a computerized medical record has proven to be an effective tool in the daily work as well for clinical, administrative and research tasks.

Humans↗

Effect of a computerized ambulatory medical record system on the validity of claims data.

Relationships were compared between claims data and charts data in a medical practice when a paper chart and manually prepared claims forms were used and after implementation of a computerized medical record system (COSTAR) in which claims data were derived automatically from the medical database. Claims data and chart data resembled each other more closely when the computer system was used, suggesting that claims data derived in this manner may have particular value in health care planning and research.

Ambulatory Care↗

An evaluation of UMLS as a controlled terminology for the Problem List Toolkit.

We are developing a set of software components--the Problem List Toolkit (PL-Tk)--to support operations on clinical problem labels. An adaptation of the National Library of Medicine's Unified Medical Language System (UMLS) provides general vocabulary services to domain-specific software components. Our initial investigation centers on the inclusion in UMLS of problem labels used in the Beth Israel Deaconess Medical Center's Online Medical Record (OMR). We also explore the semantic typing of problem labels matched in UMLS. We have operationally defined a clinical problem to derive its semantic type from classes of terms representing findings or processes typically requiring diagnostic evaluation or therapeutic management in clinical practice. Of 1262 unique OMR problem labels, 999 terms (79%) have matches in UMLS. 986 of 999 terms (99%) map to the UMLS concept of the corresponding lexical match. 952 of 999 terms (95%) have semantic types that comply with our operational definition of clinical problems. These 952 terms (75%) constitute Version 1.0 of the problem list vocabulary B196. Matching terms with inappropriate semantic types raise issues regarding requirements for PL-Tk, typing of existing UMLS terms, and the adequacy of our operational definition for clinical problems. UMLS provides a large repertoire of pre-coordinated terms that are used as problem labels in a heavily used computer-based patient record system. The semantic type hierarchy provides a framework for the consistent use of clinical concepts in problem lists such that clinical problem labels represent "good" clinical problems.

Evaluation Studies as Topic↗

Concept-match medical data scrubbing. How pathology text can be used in research.

CONTEXT: In the normal course of activity, pathologists create and archive immense data sets of scientifically valuable information. Researchers need pathology-based data sets, annotated with clinical information and linked to archived tissues, to discover and validate new diagnostic tests and therapies. Pathology records can be used for research purposes (without obtaining informed patient consent for each use of each record), provided the data are rendered harmless. Large data sets can be made harmless through 3 computational steps: (1) deidentification, the removal or modification of data fields that can be used to identify a patient (name, social security number, etc); (2) rendering the data ambiguous, ensuring that every data record in a public data set has a nonunique set of characterizing data; and (3) data scrubbing, the removal or transformation of words in free text that can be used to identify persons or that contain information that is incriminating or otherwise private. This article addresses the problem of data scrubbing. OBJECTIVE: To design and implement a general algorithm that scrubs pathology free text, removing all identifying or private information. METHODS: The Concept-Match algorithm steps through confidential text. When a medical term matching a standard nomenclature term is encountered, the term is replaced by a nomenclature code and a synonym for the original term. When a high-frequency "stop" word, such as a, an, the, or for, is encountered, it is left in place. When any other word is encountered, it is blocked and replaced by asterisks. This produces a scrubbed text. An open-source implementation of the algorithm is freely available. RESULTS: The Concept-Match scrub method transformed pathology free text into scrubbed output that preserved the sense of the original sentences, while it blocked terms that did not match terms found in the Unified Medical Language System (UMLS). The scrubbed product is safe, in the restricted sense that the output retains only standard medical terms. The software implementation scrubbed more than half a million surgical pathology report phrases in less than an hour. CONCLUSIONS: Computerized scrubbing can render the textual portion of a pathology report harmless for research purposes. Scrubbing and deidentification methods allow pathologists to create and use large pathology databases to conduct medical research.

Computing Methodologies↗

Computerized interdisciplinary assessment.

Systems and technology that support the collection and dissemination of healthcare information improve service levels, enhance quality, and improve continuity of care. The delivery of patient care is changing rapidly because sophisticated clinical information systems are providing fast, organized access to large amounts of patient information (Ornstein et al., 1997). The Carl T. Hayden Veterans Affairs Medical Center in Phoenix developed and implemented a computerized process for interdisciplinary assessment and treatment of patients. This was accomplished within the framework of a recent reorganization into service lines and the initiation of a computerized medical record system. Traditional frameworks for change management often include a methodical picture of the required steps or phases of the change process. The rapid rate of change, both technologically and in the healthcare system, however, defies the application of some aspects of traditional change theory. A model for change that considers realistic change strategies, including good planning and identification of barriers, was used to facilitate the move to computerized interdisciplinary assessment (Daly, Button, Prophet, Clarke, & Androwich, 1997).

Arizona↗

How useful is the UMLS metathesaurus in developing a controlled vocabulary for an automated problem list?

We are developing a set of problem list phrases to be used in the automated problem list of a prototype clinical computing system. Because of the large number of terms in the Unified Medical Language System (UMLS) and the links between them, we are experimenting with the use of the UMLS as the foundation for our problem list phrase set. We have found the UMLS to be very useful for this project, but that it lacks many phases clinicians wish to include in the problem list. Internal linkages between phrases provided in the UMLS are not well suited to our needs. We plan to continue our use of the UMLS but to add problem list phrases and linkages between phrases to support browsing and decision support applications.

Ambulatory Care Information Systems↗

Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.

UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion). CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.

Computer Communication Networks↗

A prototype of a computerized patient record.

Computerized medical record systems (CPRS) should present user and problem oriented views of the patient file. Problem lists, clinical course, medication profiles and results of examinations have to be recorded in a computerized patient record. Patient review screens should give a synopsis of the patient data to inform whenever the patient record is opened. Several different types of data have to be stored in a patient record. Qualitative and quantitative measurements, narratives and images are such examples. Therefore, a CPR must also be able to handle these different data types. New methods and concepts appear frequently in medicine. Thus a CPRS must be flexible enough to cope with coming demands. We developed a prototype of a computer based patient record with a graphical user interface on a SUN workstation. The basis of the system are a dynamic data dictionary, an interpreter language and a large set of basic functions. This approach gives optimal flexibility to the system. A lot of different data types are already supported. Extensions are easily possible. There is also almost no limit concerning the number of medical concepts that can be handled by our prototype. Several applications were built on this platform. Some of them are presented to exemplify the patient and problem oriented handling of the CPR.

Humans↗

Overcoming the limitations of proprietary computerized billing systems to enhance patient care.

Most physician offices have proprietary computerized billing systems, but these are not designed for monitoring utilization or addressing patient care issues, and they are difficult or impossible to modify. These systems do, however, contain valuable diagnosis and demographic information. An open-ended, relational x-base system is described that downloads this billing information and combines it with additional input to provide the practitioner with: current problem lists; medication and allergy lists; health screening reminders that are age, sex and disease specific; and commonly used demographic information. Several popular query/reporting tools are used to generate standard reports and ad hoc inquiries that relate directly to patient care. Two studies, one involving alerting physicians to possible adverse medication effects on specific patients, and one investigating appropriate use and billing of stool occult blood testing are summarized. In the constantly evolving arenas of utilization, outcomes research and cost efficiency, such an open ended, time efficient system has unlimited potential to improve patient care.

Drug-Related Side Effects and Adverse Reactions↗

Comparing natural language processing tools to extract medical problems from narrative text.

To help maintain a complete, accurate and timely Problem List, we are developing a system to automatically retrieve medical problems from free-text documents. This system uses Natural Language Processing to analyze all electronic narrative text documents in a patient's record. Here we evaluate and compare 3 different applications of NLP technology in our system: the first using MMTx (MetaMap Transfer) with a negation detection algorithm (NegEx), the second using an alpha version of a locally developed NLP application called MPLUS2, and the third using keyword searching. They were adapted and trained to extract medical problems from a set of 80 problems of diagnosis type. The version using MMTx and NegEx was improved by adding some disambiguation and modifying the negation detection algorithm, and these modifications significantly improved recall and precision. The different versions of the NLP module were compared, and showed the following recall / precision results: standard MMTx with NegEx version 0.775 / 0.398; improved MMTx with NegEx version 0.892 / 0.753; MPLUS2 version 0.693 / 0.402; and keyword searching version 0.575 / 0.807. Average results for the reviewers were a recall of 0.788 and a precision of 0.912.

Algorithms↗

Inter-rater agreement in physician-coded problem lists.

Coded problem lists will be increasingly used for many purposes in healthcare. The usefulness of coded problem lists may be limited by 1) how consistently clinicians enumerate patients' problems and 2) how consistently clinicians choose a given concept from a controlled terminology to represent a given problem. In this study, 10 physicians reviewed the same 5 clinical cases and created a coded problem list for each case using UMLS as a controlled terminology. We assessed inter-rater agreement for coded problem lists by computing the average pair-wise positive specific agreement for each case for all 10 reviewers. We also standardized problems to common terms across reviewers' lists for a given case, adjusting sequentially for synonymy, granularity, and general concept representation. Our results suggest that inter-rater agreement in unstandardized problem lists is moderate at best; standardization improves agreement, but much variability may be attributable to differences in clinicians' style and the inherent fuzziness of medical diagnosis.

Forms and Records Control↗