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Biomedical subjects

J M Teich

Publications and source records attributed to J M Teich.

52 records · Page 3Linked to original sources
PubMed1995

Enhancement of clinician workflow with computer order entry.

Physician-operated order entry systems can bring great benefits to an institution. Such systems can improve the consistency, accuracy, safety, and cost-effectiveness of orders. When building or selecting order entry for an institution, consideration must be given to the many different scenarios in which orders are written and communicated. Transfer, post-op, pre-admission and discharge orders have different communication requirements from standard inpatient orders. In certain services, orders from a very limited set (such as warfarin orders) must be written frequently for a large number of patients. Intensive-care patients, chemotherapy patients, and others have particular requirements for ordering. A computerized order entry system should respond to these requirements in order to promote correct and efficient ordering. We present a number of these issues, their specific requirements, and the approach we have taken to ensure that the system both supports and enhances workflow.

Clinical Pharmacy Information Systems↗
PubMed1994

Computerized physician order entry and quality of care.

In automated physician order entry systems, physicians enter orders directly on the computer. Compared with manual systems, advantages are that orders are legible, transcription is eliminated, the writer can be identified, and orders can rapidly be routed to their destinations. But most importantly, physician order entry allows order checking and provision of decision support to the orderer in real time. Disadvantages are that systems developed to date have been slower than pen and paper and they represent a major process change so that implementation is time-consuming and requires patience both on the part of the users and the developers.

Boston↗
PubMed1994

Coverage List: a provider-patient database supporting advanced hospital information services.

We have developed a provider-patient database system, known as Coverage List, which maintains the associations between house staff and inpatients in a teaching hospital. Coverage List automatically links each patient to the proper resident when the patient is admitted, and updates the linkage whenever the resident coverage changes due to night or weekend coverage, physician illness, changes in clinical rotations, and other factors. Using this association, decision-support applications that detect significant clinical events can transmit them directly to the responsible resident. Sign-out and patient-review systems, which collect information on all of a physician's patients, always know the patients for whom that physician is responsible. Nurses who need to contact a physician about a patient issue always know which physician is covering that patient. Coverage List also manages schedule entry and display for physicians, or for any other staff members. A physician can enter individual schedule changes, sign out her service and her pager for the day, and page consultants automatically without going through an operator. These functions support clinical practice directly and enhance the value of other clinical programs.

Algorithms↗
PubMed1994

A new knowledge structure for drug-drug interactions.

We developed a program to automatically screen patients' medication profiles for pairs of interacting drugs. Since some drug-drug interactions are indicated by changes in physiological parameters (e.g., ciprofloxacin and theophylline leading to an elevation of theophylline levels), the program considered the patients' relevant laboratory parameters prior to generating the alerts. We developed an editor to facilitate maintenance of the knowledge base. We evaluated the program for 3 weeks in two satellite pharmacies. The program reported 160 alerts of which 5 resulted in a change in the patients' therapies (one per 500 patient-days of care). These five interactions were potentially very serious. An additional 3 alerts led to changes in medication administration times. Subjectively, the program is well received and continues to be in routine clinical use.

Drug Interactions↗
PubMed1994

Impact of computerized physician order entry on physician time.

We examined the effect of computerized physician order entry on housestaff time use patterns, using time motion techniques. For both medical and surgical house officers, writing orders on the computer took about twice as long (p < 0.001), or 44 minutes for medical and 73 minutes for surgical house officers. Medical house officers recovered about half this time because some administrative tasks--e.g. looking for charts--were made easier. Within types of orders, sets of stereotyped orders took much less time with order entry, but one-time orders took longer. We have since developed strategies to make it easier to enter one-time orders.

Hospital Information Systems↗
PubMed1993

Response to a trial of physician-based inpatient order entry.

Our group has developed a physician-operated inpatient order-entry system (BICS-OE). Mindful of the problems inherent in bringing a radical cultural change such as this to the hospital, we conducted two pilots of the system prior to its full implementation. Physicians and nurses both identified a number of benefits from the use of OE. Physicians reported a number of areas where OE use was difficult or could be improved; nurses reported fewer problems. Based on the pilot trials, we modified the interface and some data constructs. These changes have enhanced the usefulness of OE in patient care in our hospital, and can serve as a model to others developing order entry.

Attitude to Computers↗
PubMed1992

Design of an easy-to-use physician order entry system with support for nursing and ancillary departments.

The Brigham Integrated Computing System (BICS) provides a broad range of clinical and administrative data-management functions for Brigham and Women's Hospital (BWH), a 720-bed major urban teaching hospital. We describe here the development of an inpatient order-entry system (BICS-OE) which is fully integrated into the BICS clinical environment. BICS-OE uses direct entry of orders by physicians to improve order response time and minimize transcription problems. BICS-OE includes a number of novel features to enhance its acceptance by physician users and its benefit to clinical practice in the hospital. Dual-mode input gives users a choice of full-screen assistance with ordering, or an intelligent text mode which provides rapid entry of orders by direct typing. An automated primary/on-call coverage by direct typing. An automated primary/on-call coverage list directs patient alerts and cosigning requests to the proper physician. Variable order sets allow a large group of orders to be stored and entered rapidly, even when some of the orders may change for different conditions. Multitasking allows the user to check prior orders and labs while concurrently entering new orders. The system permits a wide range of order checking and alerts.

Hospital Departments↗
PubMed1979

Transformation of nuclear morphology during cellular maturation.

Using in vivo labeling of mouse leukocytes with tritiated thymidine, cells in the neutrophilic series were studied to determine the change in their nuclear shape as a function of maturation level. Several morphologic shape parameters including perimeter and bending energy were used to quantify the distribution of the nuclear morphology in a given age cohort. The change in these distributions as a function of calender age level was determined. The two parameters named above were used to test the possibility of inferring the age from the quantitative morphology.

Animals↗
PubMedDate not supplied

Identifying hospital admissions due to adverse drug events using a computer-based monitor.

BACKGROUND: Hospital admissions due to adverse drug events (ADEs) are expensive, and many may be preventable, yet few institutions have ongoing surveillance for these events. OBJECTIVE: To evaluate the use of a computer-based ADE monitor to identify admissions due to ADEs and to measure the associated costs. DESIGN: Prospective cohort study in one tertiary care hospital. PARTICIPANTS: All patients admitted to nine medical and surgical units in a tertiary care hospital over an 8-month period. MAIN OUTCOME MEASURE: Admissions to the hospital due to an adverse drug event. METHODS: A computer-based monitoring program generated alerts suggesting that an ADE might be present. A trained reviewer then evaluated the record. RESULTS: Among the 3238 admissions, 76 (2.3%, 1.4% after adjusting for sampling) were found to be caused by an ADE. Of these ADEs, 78% were severe and 28% were preventable. Estimated costs were $16,177 per ADE, and $10,375 per preventable ADE; annualized costs to the hospital were $6.3 million per year for all ADEs, and $1.2 million for preventable ADEs. CONCLUSIONS: Many admissions were caused by ADEs, although our point estimate undoubtedly represents a lower bound. These events were mostly severe, often preventable, and expensive. The computer-based monitoring system represents a practical approach for identifying ADEs that occur in outpatients and cause admission to the hospital.

Adult↗
PubMedDate not supplied

Using information systems to measure and improve quality.

Information systems (IS) are increasingly important for measuring and improving quality. In this paper, we describe our integrated delivery system's plan for and experiences with measuring and improving quality using IS. Our belief is that for quality measurement to be practical, it must be integrated with the routine provision of care and whenever possible should be done using IS. Thus, at one hospital, we now perform almost all quality measurement using IS. We are also building a clinical data warehouse, which will serve as a repository for quality information across the network. However, IS are not only useful for measuring care, but also represent powerful tools for improving care using decision support. Specific areas in which we have already seen significant benefit include reducing the unnecessary use of laboratory testing, reporting important abnormalities to key providers rapidly, prevention and detection of adverse drug events, initiatives to change prescribing patterns to reduce drug costs and making critical pathways available to providers. Our next major effort will be introduce computerized guidelines on a more widespread basis, which will be challenging. However, the advent of managed care in the US has produced strong incentives to provide high quality care at low cost and our perspective is that only with better IS than exist today will this be possible without compromising quality. Such systems make feasible implementation of quality measurement, care improvement and cost reduction initiatives on a scale which could not previously be considered.

Computer Communication Networks↗
PubMedDate not supplied

Implementation of physician order entry: user satisfaction and self-reported usage patterns.

OBJECTIVES: To evaluate user satisfaction, correlates of satisfaction, and self-reported usage patterns regarding physician order entry (POE) in one hospital. DESIGN: Surveys were sent to physician and nurse POE users from medical and surgical services. RESULTS: The users were generally satisfied with POE (mean = 5.07 on a 1 to 7 scale). The physicians were more satisfied than the nurses, and the medical staff were more satisfied than the surgical staff; satisfaction levels were acceptable (more than 3.50) even in the less satisfied groups. Satisfaction was highly correlated with perceptions about POE's effects on productivity, ease of use, and speed. POE features directed at improving the quality of care were less strongly correlated with satisfaction. The physicians valued POE's off-floor accessibility most, and the nurses valued legibility and accuracy of POE orders most. Some features, such as off-floor ordering, were perceived to be highly useful and reported to be frequently used by the physicians; while other features, such as "quick mode'' ordering and personal order sets, received little self-reported use. CONCLUSIONS: Survey of POE users showed that satisfaction with POE was good. Satisfaction was more correlated with perceptions about POE's effect on productivity than with POE's effect on quality of care. Physicians and nurses constitute two very different types of users, underscoring the importance of involving both physicians and nonphysicians in POE development. The results suggest that development efforts should focus on improving system speed, adding on-line help, and emphasizing quality benefits of POE.

Attitude of Health Personnel↗
PubMedDate not supplied

How promptly are inpatients treated for critical laboratory results?

OBJECTIVE: The purpose of the study is to determine how frequently critical laboratory results (CLRs) occur and how rapidly they are acted upon. A CLR was defined as a result that met either the critical reporting criteria used by the laboratory at Brigham and Women's Hospital or other, more complex criteria. DESIGN: This is a retrospective cohort study in a large academic tertiary-care hospital. MEASUREMENTS: The proportion of chemistry and hematology results obtained in a 13-day period that met the hospital laboratory's critical reporting criteria were calculated. The charts of a stratified random sample of patients with CLRs due to sodium, potassium, and glucose were reviewed to determine the time interval until an appropriate treatment was ordered and the time interval until the critical condition was resolved. RESULTS: In 13 days, 1938 of 201,037 laboratory results (0.96%, or 0.44 per patient-day) met the hospital's critical reporting criteria. In the chart review, 222 CLRs were included in the stratified random sample, and 99 of these met the inclusion criteria. Among these 99 CLRs, the median time interval until an appropriate treatment was ordered was 2.5 hours. This interval was 1.8 hours when the CLR met the laboratory's criteria and a phone call was made, and 2.8 hours when the CLR met more complex criteria not requiring a phone call (p = 0.07). For 27 (27%) of the CLRs, an appropriate treatment was ordered only after five or more hours. The median time until the condition resolved was 14.3 hours: 12.0 hours for CLRs that met the hospital's criteria and 20.9 hours for the CLRs that met the more complex criteria (p = 0.006). CONCLUSION: Although CLRs meeting the hospital's criteria were reported promptly by the laboratory, treatment delays were still common. Results that did not meet the hospital's critical criteria but still represented serious clinical situations were more often associated with treatment delays. Difficulty communicating critical results directly to the responsible caregiver is the likely cause of some delays in treatment. New communications methods, including computer-based technologies, should be explored and tested for their potential to reduce treatment delays and improve clinical care.

Academic Medical Centers↗
PubMedDate not supplied

Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report.

BACKGROUND: Adverse drug events (ADEs) are both common and costly. Most hospitals identify ADEs using spontaneous reporting, but this approach lacks sensitivity; chart review identifies more events but is expensive. Computer-based approaches to ADE identification appear promising, but they have not been directly compared with chart review and they are not widely used. OBJECTIVES: To develop a computer-based ADE monitor, and to compare the rate and type of ADEs found with the monitor with those discovered by chart review and by stimulated voluntary report. DESIGN: Prospective cohort study in one tertiary-care hospital. PARTICIPANTS: All patients admitted to nine medical and surgical units in a tertiary-care hospital over an eight-month period. MAIN OUTCOME MEASURE: Adverse drug events identified by the computer-based monitor, by chart review, and by stimulated voluntary report. METHODS: A computer-based monitoring program identified alerts, which were situations suggesting that an ADE might be present (e.g., an order for an antidote such as naloxone). A trained reviewer then examined patients' hospital records to determine whether an ADE had occurred. The results of the computer-based monitoring strategy were compared with two other ADE detection strategies: intensive chart review and stimulated voluntary report by nurses and pharmacists. The monitor and the chart review strategies were independent, and the reviewers were blinded. RESULTS: The computer monitoring strategy identified 2,620 alerts, of which 275 were determined to be ADEs. The chart review found 398 ADEs, whereas voluntary report detected 23. Of the 617 ADEs detected by at least one method, 76 ADEs were detected by both computer monitor and chart review. The computer monitor identified 45 percent; chart review, 65 percent; and voluntary report, 4 percent. The ADEs identified by computer monitor were more likely to be classified as "severe" than were those identified by chart review (51 versus 42 percent, p = .04). The positive predictive value of computer-generated alerts was 16 percent during the first eight weeks of the study; rule modifications increased this to 23 percent in the final eight weeks. The computer strategy required 11 person-hours per week to execute, whereas chart review required 55 person-hours per week and voluntary report strategy required 5. CONCLUSIONS: The computer-based monitor identified fewer ADEs than did chart review but many more ADEs than did stimulated voluntary report. The overlap among the ADEs identified using different methods was small, suggesting that the incidence of ADEs may be higher than previously reported and that different detection methods capture different events. The computer-based monitoring system represents an efficient approach for measuring ADE frequency and gauging the effectiveness of ADE prevention programs.

Adverse Drug Reaction Reporting Systems↗
PubMedDate not supplied

Improving response to critical laboratory results with automation: results of a randomized controlled trial.

OBJECTIVE: To evaluate the effect of an automatic alerting system on the time until treatment is ordered for patients with critical laboratory results. DESIGN: Prospective randomized controlled trial. INTERVENTION: A computer system to detect critical conditions and automatically notify the responsible physician via the hospital's paging system. PATIENTS: Medical and surgical inpatients at a large academic medical center. One two-month study period for each service. MAIN OUTCOMES: Interval from when a critical result was available for review until an appropriate treatment was ordered. Secondary outcomes were the time until the critical condition resolved and the frequency of adverse events. METHODS: The alerting system looked for 12 conditions involving laboratory results and medications. For intervention patients, the covering physician was automatically notified about the presence of the results. For control patients, no automatic notification was made. Chart review was performed to determine the outcomes. RESULTS: After exclusions, 192 alerting situations (94 interventions, 98 controls) were analyzed. The intervention group had a 38 percent shorter median time interval (1.0 hours vs. 1.6 hours, P = 0.003; mean, 4.1 vs. 4.6 hours, P = 0.003) until an appropriate treatment was ordered. The time until the alerting condition resolved was less in the intervention group (median, 8.4 hours vs. 8.9 hours, P = 0.11; mean, 14.4 hours vs. 20.2 hours, P = 0.11), although these results did not achieve statistical significance. The impact of the intervention was more pronounced for alerts that did not meet the laboratory's critical reporting criteria. There was no significant difference between the two groups in the number of adverse events. CONCLUSION: An automatic alerting system reduced the time until an appropriate treatment was ordered for patients who had critical laboratory results. Information technologies that facilitate the transmission of important patient data can potentially improve the quality of care.

Academic Medical Centers↗
PubMedDate not supplied

The impact of computerized physician order entry on medication error prevention.

BACKGROUND: Medication errors are common, and while most such errors have little potential for harm they cause substantial extra work in hospitals. A small proportion do have the potential to cause injury, and some cause preventable adverse drug events. OBJECTIVE: To evaluate the impact of computerized physician order entry (POE) with decision support in reducing the number of medication errors. DESIGN: Prospective time series analysis, with four periods. SETTING AND PARTICIPANTS: All patients admitted to three medical units were studied for seven to ten-week periods in four different years. The baseline period was before implementation of POE, and the remaining three were after. Sophistication of POE increased with each successive period. INTERVENTION: Physician order entry with decision support features such as drug allergy and drug-drug interaction warnings. MAIN OUTCOME MEASURE: Medication errors, excluding missed dose errors. RESULTS: During the study, the non-missed-dose medication error rate fell 81 percent, from 142 per 1,000 patient-days in the baseline period to 26.6 per 1,000 patient-days in the final period (P < 0.0001). Non-intercepted serious medication errors (those with the potential to cause injury) fell 86 percent from baseline to period 3, the final period (P = 0.0003). Large differences were seen for all main types of medication errors: dose errors, frequency errors, route errors, substitution errors, and allergies. For example, in the baseline period there were ten allergy errors, but only two in the following three periods combined (P < 0.0001). CONCLUSIONS: Computerized POE substantially decreased the rate of non-missed-dose medication errors. A major reduction in errors was achieved with the initial version of the system, and further reductions were found with addition of decision support features.

Clinical Pharmacy Information Systems↗