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At least 19 recordsLinked to original sources
PubMedDate not supplied

The accuracy of medication data in an outpatient electronic medical record.

OBJECTIVE: To measure the accuracy of medication records stored in the electronic medical record (EMR) of an outpatient geriatric center. The authors analyzed accuracy from the perspective of a clinician using the data and the perspective of a computer-based medical decision-support system (MDSS). DESIGN: Prospective cohort study. METHODS: The EMR at the geriatric center captures medication data both directly from clinicians and indirectly using encounter forms and data-entry clerks. During a scheduled office visit for medical care, the treating clinician determined whether the medication records for the patient were an accurate representation of the medications that the patient was actually taking. Using the available sources of information (the patient, the patient's vials, any caregivers, and the medical chart), the clinician determined whether the recorded data were correct, whether any data were missing, and the type and cause for each discrepancy found. RESULTS: At the geriatric center, 83% of medication records represented correctly the compound. dose, and schedule of a current medication; 91% represented correctly the compound. 0.37 current medications were missing per patient. The principal cause of errors was the patient (36.1% of errors), who misreported a medication at a previous visit or changed (stopped, started, or dose-adjusted) a medication between visits. The second most frequent cause of errors was failure to capture changes to medications made by outside clinicians, accounting for 25.9% of errors. Transcription errors were a relatively ucommon cause (8.2% of errors). When the accuracy of records from the center was analyzed from the perspective of a MDSS, 90% were correct for compound identity and 1.38 medications were missing or uncoded per patient. The cause of the additional errors of omission was a free-text "comments" field-which it is assumed would be unreadable by current MDSS applications-that was used by clinicians in 18% of records to record the identity of the medication. CONCLUSIONS: Medication records in an outpatient EMR may have significant levels of data error. Based on an analysis of correctable causes of error, the authors conclude that the most effective extension to the EMR studied would be to expand its scope to include all clinicians who can potentially change medications. Even with EMR extensions, however, ineradicable error due to patients and data entry will remain. Several implications of ineradicable error for MDSSs are discussed. The provision of a free-text "comments" field increased the accuracy of medication lists for clinician users at the expense of accuracy for a MDSS.

Aged↗
PubMed1993

Real and imagined barriers to an electronic medical record.

We developed an electronic medical record for ambulatory patients as part of the integrated clinical information system at Beth Israel Hospital. During the four years since it was installed, clinicians have entered 76,060 patient problems, 137,713 medications, and 33,938 notes. Residents, who had to type notes in themselves, entered 49.5% of their notes into OMR. Several factors that we had predicted would be barriers to an electronic medical record, such as clinician reluctance to type or perform data entry, have not proved to be significant problems. Other anticipated barriers, such as difficulties with dual charting on paper during transition to an electronic medical record, have been realized. The major unexpected barrier that has been encountered is increased clinician concern about the privacy and security of full text notes relative to other data elements in the clinical information system. We have attempted to modify the electronic medical record so as to overcome some of these barriers.

Attitude to Computers↗
PubMed1995

A document processing architecture for electronic medical records.

An Electronic Medical Record System (EMRS) requires a deep analysis of the clinical workplace and of the character and uses of the medical record itself. Charts are a diverse and useful collection of loosely structured specialized documents, each with an orderly outline, but widely divergent and unpredictable contents--the more critical the case, the more unpredictable the details. This can be achieved by adapting an underlying information architecture that is based on document processing (rather than data processing), using the logic and conventions of text tagging, as in Mosaic, the Standard Generalized Markup Language (SGML), and HyTime.

Medical Records↗
PubMedDate not supplied

Medical electronics in surgery.

I have described the results of a telemetering radio capsule which we used in the clinical assessment of digestive disease. This method has many merits which we have never found with other methods. This is one example of the clinical use of medical electronics. Medical electronics provides surgeons with many useful aids to improve diagnosis and treatment of their patients. Medical electronic technics should be used more often.

Female↗
PubMed1994

A high-level object-oriented model for representing relationships in an electronic medical record.

The importance of electronic medical records to improve the quality and cost-effectiveness of medical care continues to be realized. This growing importance has spawned efforts at defining the structure and content of medical data, which is heterogeneous, highly inter-related, and complex. Computer-assisted data modeling tools have greatly facilitated the process of representing medical data, however the complex inter-relationships of medical information can result in data models that are large and cumbersome to manipulate and view. This report presents a high-level object-oriented model for representing the relationships between objects or entities that might exist in an electronic medical record. By defining the relationship between objects at a high level and providing for inheritance, this model enables relating any medical entity to any other medical entity, even though the relationships were not directly specified or known during data model design.

Databases, Factual↗
PubMed1993

A framework for modelling the electronic medical record.

This paper presents a model for an electronic medical record which satisfies the requirements for a faithful and structured record of patient care set out in a previous paper in this series. The model underlies the PEN & PAD clinical workstation, and it provides for a permanent, completely attributable record of patient care and the process of medical decision making. The model separates the record into two levels: direct observations of the patient and meta-statements about the use of observations in decision making and the clinical dialogue. The model is presented in terms of "descriptions" formulated in the Structured Meta Knowledge (SMK) formalism, but many of its features are more general than the specific implementation. The use of electronic medical records based on the model for decision support and the analysis of aggregated data are discussed along with potential use of the model in distributed information systems.

Computer Simulation↗
PubMedDate not supplied

Privacy, confidentiality, and electronic medical records.

The enhanced availability of health information in an electronic format is strategic for industry-wide efforts to improve the quality and reduce the cost of health care, yet it brings a concomitant concern of greater risk for loss of privacy among health care participants. The authors review the conflicting goals of accessibility and security for electronic medical records and discuss nontechnical and technical aspects that constitute a reasonable security solution. It is argued that with guiding policy and current technology, an electronic medical record may offer better security than a traditional paper record.

Computer Security↗
PubMed1994

The electronic medical record: perspective from Mayo Clinic.

On 1 January 1993, the Electronic Medical Record Task Force at Mayo Clinic published its report. Charged by the Mayo Foundation to define the Electronic Medical Record (EMR) for Mayo, the task force mapped the goals, strategies and time-lines for implementation of the EMR in that institution. The task force was composed predominantly of caregivers (physicians and nurses) with assistance from members of Mayo's information systems and administrative departments. The focus of the effort was care of the patient with the consensus belief that the EMR will improve that process and, if designed robustly, will serve the other information needs of claims, research, education and practice management. The recommendations of this report have been accepted by the Mayo Foundation leadership resulting in the generation of a master plan and the creation of the governance structure for implementation at Mayo. This paper abstracts key portions of the report.

Computer Systems↗