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Priming intelligent split menus with text corpora for computerized patient record data-entry.

This paper describes a methodology for using the knowledge in existing health care text corpora to prime intelligent split menus for provider data-entry. A split menu is one where the top portion of a menu list is organized by user-selection frequency and the bottom portion of the list is traditionally organized in alphabetical order. A simulation shows that data-entry with these intelligent split menus requires between two and five times less effort (in terms of user selections or mouse clicks) than menus arranged alphabetically. This paper uses a corpus from echocardiography to develop the simulation. The methodology uses statistical associations between word categories in the corpus such as 'anatomy' or 'pathology' to prime the frequency ordering of the menus. A dictionary of terms contains the categorical information. After the initial priming, actual user selections are used to update the frequencies used to adapt to providers' individual data-entry patterns.

Computer Simulation↗

Failure of computerized treatment suggestions to improve health outcomes of outpatients with uncomplicated hypertension: results of a randomized controlled trial.

STUDY OBJECTIVE: To assess the effects of evidence-based treatment suggestions for hypertension made to physicians and pharmacists using a comprehensive electronic medical record system. DESIGN: Randomized controlled trial with a 2 x 2 factorial design of physician and pharmacist interventions, which resulted in four groups of patients: physician intervention only, pharmacist intervention only, intervention by physician and pharmacist, and intervention by neither physician nor pharmacist (control). SETTING: Academic primary care internal medicine practice. SUBJECTS: Seven hundred twelve patients with uncomplicated hypertension. MEASUREMENTS AND MAIN RESULTS: Suggestions were displayed to physicians on computer workstations used to write outpatient orders and to pharmacists when filling prescriptions. The primary end point was generic health-related quality of life. Secondary end points were symptom profile and side effects from antihypertensive drugs, number of emergency department visits and hospitalizations, blood pressure measurements, patient satisfaction with physicians and pharmacists, drug therapy compliance, and health care charges. In the control group, implementation of care changes in accordance with treatment suggestions was observed in 26% of patients. In the intervention groups, compliance with suggestions was poor, with treatment suggestions implemented in 25% of patients for whom suggestions were displayed only to pharmacists, 29% of those for whom suggestions were displayed only to physicians, and 35% of the group for whom both physicians and pharmacists received suggestions (p=0.13). Intergroup differences were neither statistically significant nor clinically relevant for generic health-related quality of life, symptom and side-effect profiles, number of emergency department visits and hospitalizations, blood pressure measurements, charges, or drug therapy compliance. CONCLUSION: Computer-based intervention using a sophisticated electronic physician order-entry system failed to improve compliance with treatment suggestions or outcomes of patients with uncomplicated hypertension.

Antihypertensive Agents↗

Leapfrog Group: a prince in disguise or just another frog?

Executive directors, presidents, chief executive officers of some of the principal nursing organizations, and other nurse leaders in the United States were interviewed to ascertain their reactions to the recommendations of the Leapfrog Group, a coalition of more than 90 companies that employ large numbers of workers in the United States. The Leapfrog Group is concerned with patient safety and was formed in response to the Institute of Medicine's report on health care errors. Three recommendations for urban hospitals focus on computerized physician order entry, evidence-based hospital referral, and the use of intensivists.

Attitude of Health Personnel↗

Categorization of free-text problem lists: an effective method of capturing clinical data.

Problem lists assist in organizing patient information in computer based medical records. However, in order to use problem lists for billing, research, decision support and standardization, a categorization of the problems entered is required. We describe the problem list component of our computerized patient record, the On-line Medical Record (OMR), which combines a free-text entry mechanism with a categorization scheme, using a dictionary containing 846 terms. All 118,040 problems entered during the system's six years of use have been analyzed, 477 clinicians have entered a mean +/- S.D. of 238 +/- 604 problems into 22,311 patient records. The average number of problems in each patient's file was 5.1 +/- 3.9. Comments were typed for 80,281 (68%) of the problems, ranging in length from 1 to 2456 characters, with a mean length of 98 +/- 110 characters. Half the problems were entered on the day of the encounter with the patient. Overall, 66% of all problems were categorized in relation to terms from the problem dictionary. Lexical analysis of all problem names showed that 80% could be mapped to Meta 1.4, Snomed 3.0 or a pre-release version of Read 3.0. We conclude that a problem list entry scheme combining free-text entry and optional categorization using a dictionary can result in a high proportion of problems being categorized as desired. Improvement of the system by elimination of unused dictionary terms and addition of 1000 terms identified by the lexical analysis is likely to result in even higher categorization rates.

Humans↗

End of visit: design considerations for an ambulatory order entry module.

Current paper-based processes for performing billing documentation and test ordering at the end of a clinic visit are fraught with problems, resulting in numerous workflow inefficiencies and significant revenue losses for a healthcare organization. Paper forms are often filled out inaccurately or incompletely, or can be misrouted or lost. Computerizing these processes can alleviate many of these problems. We are building a new module for our ambulatory electronic medical record system to automate these "end of visit" (EOV) activities, which includes completing encounter forms, ordering lab and diagnostic tests, and printing patient visit summaries. An EOV module must be carefully designed to incorporate the needs of clinicians, front office staff, ancillary labs, medical records, and finance departments. An optimally designed EOV system should be customizable to fit into the clinician's workflow, and should help reduce financial losses, improve clinical documentation, and reduce workflow inefficiencies.

Ambulatory Care Information Systems↗

Using Failure Mode and Effects Analysis for safe administration of chemotherapy to hospitalized children with cancer.

BACKGROUND: The administration of chemotherapy to hospitalized children with cancer is a complex and high-risk process. A team divided the process into three areas--prescribing, dispensing, and administration--and used Failure Mode and Effects Analysis (FMEA) to identify the elements of risk and implement appropriate strategies. For each area, potential failures within subprocesses were assigned risk priority numbers (RPNs), reflecting their frequency, severity, and detectability. STRATEGIES FOR RISK REDUCTION: The team made prescribing and administration, the areas with the highest RPNs, the focus of most of its strategies, which were introduced and completed in 2002. POSTIMPLEMENTATION RESULTS: The potential prescribing error rate decreased from 23% to 14%; use of preprinted standard order sets increased from 22% to 45% in 2003 (one year after the FMEA was conducted) and 76% in 2005. Actual dispensing errors decreased from 3 to 1, and the actual administration errors from 4 to 3. FINAL REFLECTIONS: Computerized order entry systems would only affect prescribing, dispensing, and administering, which would still be done manually, resulting in potential for failure. The FMEA project will be an ongoing part of providing safe chemotherapy treatments.

Antineoplastic Agents↗

Professional agendas and computer-based patient records: negotiating for control.

Professional groups approach the computer implementation process as an opportunity both to protect and enhance their ability to perform their work and to negotiate for arrangements that will further their professional agendas. The article examines and compares the professional agendas and negotiating positions of pharmacy and laboratory departments as examples of professional groups involved in the implementation of order entry systems. Findings showed a stronger stance taken by the pharmacy departments in both institutions to exercise as much control as possible over the order entry process. This stance is influenced by both task issues and professional agendas. Understanding the link between these professional agendas and the issues that arise during computer implementation can help guide similar negotiations as institutions adopt computer-based patient record systems.

Hospital Departments↗

A cross-site qualitative study of physician order entry.

OBJECTIVE: To describe the perceptions of diverse professionals involved in computerized physician order entry (POE) at sites where POE has been successfully implemented and to identify differences between teaching and nonteaching hospitals. DESIGN: A multidisciplinary team used observation, focus groups, and interviews with clinical, administrative, and information technology staff to gather data at three sites. Field notes and transcripts were coded using an inductive approach to identify patterns and themes in the data. MEASUREMENTS: Patterns and themes concerning perceptions of POE were identified. RESULTS: Four high-level themes were identified: (1) organizational issues such as collaboration, pride, culture, power, politics, and control; (2) clinical and professional issues involving adaptation to local practices, preferences, and policies; (3) technical/implementation issues, including usability, time, training and support; and (4) issues related to the organization of information and knowledge, such as system rigidity and integration. Relevant differences between teaching and nonteaching hospitals include extent of collaboration, staff longevity, and organizational missions. CONCLUSION: An organizational culture characterized by collaboration and trust and an ongoing process that includes active clinician engagement in adaptation of the technology were important elements in successful implementation of physician order entry at the institutions that we studied.

Attitude of Health Personnel↗

Respiratory care in the computer age.

Computerization in health care is rapidly advancing and is improving patient safety (eg, computerized physician order entry decreases the frequency of medical errors) and practitioner effectiveness and efficiency. Computerization and other developing technologies raise concern about the privacy of health information. In 1996 Congress passed the Health Insurance Portability and Accountability Act (HIPAA), which included privacy provisions that went into effect in April 2003. HIPAA has important impacts on health care providers. With the tremendous growth of health care information systems comes the need to standardize the storage and sharing of health information, so there is an initiative underway to develop a National Health Information Infrastructure, which will set standards for health information exchange among consumers, providers, and the public health sector, as well as consolidate the "silos" of health information that are in place today.

Computer Security↗

Computerized prescribing: building the electronic infrastructure for better medication usage.

Computerized prescribing in the practice of medicine is a change that is overdue. Virtually all prescriptions in the United States are still handwritten. Instead, medications should be ordered on a computer interacting with 3 databases: patient drug history, scientific drug information and guideline reference, and patient-specific (weight, laboratory) data. Current problems with prescribing on which computerized prescribing could have a positive impact include (1) drug selection; (2) patient role in pharmacotherapy risk-benefit decision making; (3) screening for interactions (drug-drug, drug-laboratory, drug-disease); (4) linkages between laboratory and pharmacy; (5) dosing calculations and scheduling; (6) coordination between team members, particularly concerning patient education; (7) monitoring and documenting adverse effects; and (8) postmarketing surveillance of therapy outcomes. Computerized prescribing is an important component of clinician order entry. Development of this tool has been impeded by a number of conceptual, implementation, and policy barriers. Overcoming these constraints will require clinically and professionally guided vision and leadership.

Clinical Laboratory Information Systems↗

The diagnostic headache diary--a headache expert system.

The purpose with the headache expert system was to develop a diagnostic and educational tool for the general practitioners (GP), in order to improve their management of headache disorders. This chapter describes the diagnostic headache diary, an expert system intended for education and diagnosing headache disorders. The diagnostic headache diary can be printed and used by headache patients at home to record their headache attacks and medicine consumption by a day to day basis. It can also be used by the GP at the clinic in order to provide the headache diagnoses. The diagnostic headache diary was written in the programming language Delphi and implemented as a stand-alone Windows 95 program. It was integrated as one of five modules in the headache tutorial described in another chapter of this book. The diagnostic headache diary consists of four modules: Patient data, diary, medication and diagnosis. Configured around a database, the diagnostic headache diary allows entries of several patients and search facilities. The diagnostic headache diary was tested and validated by entering expert-diagnosed data from different headache disorders which where compared to the computer generated diagnoses.

Diagnosis, Computer-Assisted↗

El Camino Hospital: using health information technology to promote patient safety.

BACKGROUND: El Camino Hospital is a leader in the use of health information technology to promote patient safety, including bar coding, computerized order entry, electronic medical records, and wireless communications. OVERALL APPROACH TO QUALITY AND SAFETY: Each year, El Camino Hospital's board of directors sets performance expectations for the chief executive officer, which are tied to achievement of local, regional, and national safety and quality standards, including the six Institute of Medicine quality dimensions. He then determines a set of explicit quality goals and measurable actions, which serve as guidelines for the overall hospital. The goals and progress reports are widely shared with employees, medical staff, patients and families, and the public. ADDRESSING THE SIX IOM QUALITY AIMS: For safety, for example, the medication error reduction team tracks and reviews medication error rates. The hospital has virtually eliminated transcription errors through its 100% use of computerized physician order entry. Clinical pathways and standard order sets have reduced practice variation, providing a safer environment. CHALLENGES: Many projects focused on timeliness, such as emergency department wait time, lab turnaround time, and pneumonia time to initial antibiotic. Results have been mixed, with projects most successful when a link was established with patient outcomes, such as in reducing time to percutaneous transluminal coronary angioplasty for patients with acute myocardial infarction.

Adverse Drug Reaction Reporting Systems↗

Preparation and use of preconstructed orders, order sets, and order menus in a computerized provider order entry system.

OBJECTIVE: To describe the configuration and use of the computerized provider order entry (CPOE) system used for inpatient and outpatient care at the authors' facility. DESIGN: Description of order configuration entities, use patterns, and configuration changes in a production CPOE system. MEASUREMENTS: The authors extracted and analyzed the content of order configuration entities (order dialogs, preconfigured [quick] orders, order sets, and order menus) and determined the number of orders entered in their production order entry system over the previous three years. The authors measured use of these order configuration entities over a six-month period. They repeated the extract two years later to measure changes in these entities. RESULTS: CPOE system configuration, conducted before and after first production use, consisted of preparing 667 order dialogs, 5,982 preconfigured (quick) orders, and 513 order sets organized in 703 order menus for particular contexts, such as admission for a particular diagnosis. Fifty percent of the order dialogs, 57% of the quick orders, and 13% of the order sets were used within a six-month period. Over the subsequent two years, the volume of order configuration entities increased by 26%. CONCLUSIONS: These order configuration steps were time-consuming, but the authors believe they were important to increase the ordering speed and acceptability of the order entry software. Lessons learned in the process of configuring the CPOE ordering system are given. Better understanding of ordering patterns may make order configuration more efficient because many of the order configuration entities that were created were not used by clinicians.

Hospital Information Systems↗

A new approach to the implementation of direct care-provider order entry.

Successful implementation of direct computer-based care-provider order entry traditionally relies on one of two different approaches development from scratch or installation of a commercial product. The former requires extensive resources; the latter, by its proprietary nature, limits extension of the system beyond capabilities supplied by the vendor. This paper describes an intermediate approach using the association of a locally-developed and controlled set of distributed microcomputer based applications and a commercial, mainframe-based order entry application used as an order transaction processing system. This combination provides both an intuitive user interface and a platform for implementing clinical decision-support tools.

Computer Communication Networks↗

Evaluating the UMLS as a source of lexical knowledge for medical language processing.

Medical language processing (MLP) systems rely on specialized lexicons in order to recognize, classify, and normalize medical terminology, and the performance of an MLP system is dependent on the coverage and quality of such lexicons. However, the acquisition of lexical knowledge is expensive and time-consuming. The UMLS is a comprehensive resource that can be used to acquire lexical knowledge needed for medical language processing. This paper describes methods that use these resources to automatically create lexical entries and generate two lexicons. The first lexicon was created primarily using the UMLS, whereas the second was created by supplementing the lexicon of an existing MLP system called MedLEE with entries based on the UMLS. We subsequently carried out a study, which is the primary focus of this paper, using MedLEE with each of the two lexicons and also the current MedLEE lexicon to measure performance. Overall accuracy, sensitivity, and specificity using the lexicon primarily based on the UMLS were.86,.60, and.96 respectively. Those measures using the MedLEE lexicon alone were.93,.81, and.93, which was significantly better except for specificity; performance using the supplemental lexicon was exactly the same as performance using solely the MedLEE lexicon.

Natural Language Processing↗

Seven years experience with a computerized diabetes clinic database.

With the emergence of information technology applications in medicine, a computerized medical record system that could be used to : (1) maintain patients' clinical records over time, (2) communicate with referring practitioners, and (3) form the basis of a potential research database of information, was sought. In 1987, we developed such a clinical database to register patients attending our busy Diabetes Clinic, now seeing in excess of 300 new referrals and, on average, 3,000 clinic visits per year. Baseline demographic data, clinical history, and examination and investigation results are recorded. We also record diabetes therapy and other medication dosage and changes, monitor follow-up, assess health outcome information (such as stroke or amputation), and generate results, summaries, and reports to referring practitioners and other health professionals. We now have almost seven years of experience using the system. Initially established on a single PC with paper-based data collection and subsequent data entry (running as a DOS application), it is now established on a PC Local Area Network [LAN] with terminals in the clinic consultation rooms enabling direct data entry and allowing patients to view their results in graphic form on screen. From its inception, the Diabetes Clinic Database System has maintained patient demographic and clinical data (which facilitates efficient clinic management) with patient clinic lists and adhesive address labels generated from appropriate menus. Batch mode processing produces daily work sheets which facilitate the running of clinics as well as ad hoc, daily, and weekly reports for all patients (as required). This expedites correspondence with referring doctors. A quality assurance report to the clinic doctor highlights missing clinical information which must be obtained in order to ensure data completeness. The initial system was relatively inefficient in that it required data entry following patient consultation and provided no immediate feedback to patients themselves. In January 1994, to address these deficiencies, the system was established on a PC LAN (running under Novell); it provided on-line data entry within the clinic setting and enabled patients to participate in the recording of their information, observe their own progress by way of on-screen graphs (e.g., blood sugar control, weight, cholesterol), and receive hand-held summaries generated immediately following the clinic visit. Batch programs generate hard copies of this data to be filed in medical records. Two major assessments of the system have been undertaken. In February 1990, we undertook a survey of Local Doctors with 5 or more patient referrals on the system; this resulted in a pleasing 66% response rate. There was an almost universal acceptance and indeed a significant preference for this system over 'traditional' letters. In January 1994, following the introduction of the system onto the PC LAN for direct data entry in the clinic setting, we assessed (by anonymous questionnaire at the end of the consultation) patient attitudes towards these changes. The development of the CRS Diabetes Clinic Database System has improved our approach to diabetes outpatient care and our communication with other health professionals. It has the added benefit of providing a database of information that is suitable to address critical clinical research issues in diabetes management. This system provides an acceptable blend of information technology and clinical medicine, redesigning and enhancing the way we deliver medical care to patients with diabetes. Involving the patient in the collection and interpretation of their clinical data via a computer system (as utilized within our clinical unit), is both acceptable to the patient and her referring doctor. Ongoing system refinement and assessment remains integral to our use of information technology.

Australia↗

The training and use of an artificial neural network to monitor use of medication in treatment of complex patients.

An artificial-neural-network-based drug interaction warning system was developed for use with a computerized real-time entry medical records system. The goal of the study was to provide physicians and nurses with timely warnings of potential drug interactions as therapies were prescribed. In a dialysis unit, physicians and clinical pharmacists defined rules of proper drug therapy, then trained a neural network with those rules. When the network was used to review the therapies of this patient population, a number of inconsistencies were discovered, and medication orders were changed on several patients. Real-time implementation of this monitoring system could provide messages to assure that drug therapy is consistent and proper, according to rules created by the providers of healthcare, thus preventing occasional mistakes in drug therapy.

Drug Interactions↗