Eric G. Poon

Patient Safety Concerns Arising from Test Results That Return after Hospital Discharge

Context Poor communication between inpatient and outpatient providers precedes many preventable adverse events that occur shortly after discharge. Contribution Forty-one percent of 2644 patients on the hospitalist services of 2 academic hospitals had pending laboratory or radiology results at discharge. Physician-reviewers deemed approximately 9% of these results potentially actionable. Physician surveys done 14 days after results were first available showed that physicians were unaware of many results and thought that about 13% of them required urgent action. Cautions Findings may not apply to nonacademic or nonhospitalist settings. Implications We need good integrated systems to assure follow-up of tests that are pending at discharge. The Editors Good communication between inpatient and outpatient physicians at the transition from hospital to home is critical to patient safety. However, the amount and complexity of information that must be relayed at hospital discharge are often overwhelming. Unfortunately, when communication breaks down, patients are at risk: More than half of all preventable adverse events occurring soon after hospital discharge have been related to poor communication among providers (1). Recently, the challenges to high-quality transitions of care have been increasingly recognized (2), and several factors may be contributing to communication failures at discharge. Although the introduction of hospitalist programs across the United States has produced positive results (3-5), the discontinuity of care inherent in the hospitalist model increases the likelihood of communication failures and makes thorough communication at discharge essential (6). Discontinuity is also an issue in teaching hospitals, where physicians-in-training may be responsible for some or all of the communication at discharge and, under new work-hour restrictions, may frequently change services or work in shifts. Whatever the cause, discontinuity of care at the inpatient-to-outpatient transition has been shown to be associated with medical errors (7). Among these errors is a failure to follow up on the results of laboratory tests and radiologic studies that return after discharge. Although timely follow-up on test results has received attention from the Agency for Healthcare Research and Quality (8) and failure to follow up on results has been recognized by a large malpractice insurer (9) as accounting for one quarter of diagnosis-related malpractice cases, few studies have addressed follow-up on test results pending at hospital discharge. Moore and colleagues (7) studied test follow-up errors, which were defined as having a test result noted as pending at discharge in the inpatient medical record but not acknowledged in the outpatient chart. Using retrospective chart review, they found this type of error in the records of 8% of all discharged patients and 41% of all patients discharged with pending test results, but their study design did not allow them to determine 1) whether clinicians were aware of the results and did not document them or 2) the clinical consequences of these errors. To our knowledge, no other studies have prospectively examined the prevalence and characteristics of test results that return after discharge or physician awareness of them. We hypothesized that test results pending at discharge are frequently overlooked in the handoff from the inpatient physician to the outpatient physician and that some of these results might have important clinical consequences for patients. Accordingly, we sought to prospectively determine the prevalence and characteristics of these potentially actionable results, to determine how often physicians are unaware of these results, and to evaluate the satisfaction of inpatient physicians with current systems for following up on results returning after discharge. Methods We carried out our study on the general medicine hospitalist services at 2 academic tertiary care centers in Boston, Massachusetts (hospitals A and B). The human research committee for both hospitals reviewed and approved the study design. The hospitals belong to the same integrated care-delivery network and share a common electronic clinical data repository that includes test results, discharge orders and summaries, ambulatory notes, and medication and problem lists. These data are accessible at all inpatient and outpatient sites through the same electronic medical record. In addition, all physicians use the same e-mail system. Hospital A has 3 hospitalist inpatient teams that each consist of 1 hospitalist attending physician, 1 internal medicine resident, and 2 interns. At hospital A, the hospitalist attending physician is usually responsible for all communication to outpatient physicians at discharge, as well as for follow-up on all pending test results that return after discharge. Hospital B has 2 types of hospitalist services. One is nonhousestaff and is staffed only by hospitalist and nonhospitalist attending physicians; the nonhospitalist attending physicians care for their own patients on this service, but for the purposes of the study, we categorized them as inpatient physicians. The other hospitalist service at hospital B is a teaching service of 4 teams, each with 1 hospitalist attending physician, 1 junior resident, and 3 interns. On these teams at hospital B, the junior resident is responsible for communication at discharge and follow-up on all pending test results. During the study, 16 hospitalists were responsible for patient discharges at hospital A, 15 hospitalist and 93 nonhospitalist attending physicians were responsible for discharges on the nonhousestaff service at hospital B, and 54 junior residents were responsible for discharges on the teaching service at hospital B. Patient Selection and Identification of Results Returning after Discharge Using the hospital computer systems, we prospectively identified 2644 consecutive patients discharged from February to June 2004. Shortly after each patients discharge, a research assistant entered into a database the patients identifying information, discharge diagnosis, and times and dates of hospital admission and discharge. He or she then tracked each patients pending test results by entering the patient on a watch list using a feature in a results-management system called Results Manager. Results Manager is a computer application that is fully integrated into the electronic medical record and is able to cull pending and final test results from the clinical data repository and to prioritize them on the basis of type of result and degree of abnormality. It was originally developed to track test results in the outpatient setting, and it has been evaluated and tested extensively in that setting but has not been used for inpatients (10). Data Collection We tracked test results with Results Manager for 14 days after patient discharge. A research assistant screened all laboratory and radiologic test results returning after discharge and excluded the results of tests done after discharge. Normal, near-normal, and stable results were excluded by using a predefined algorithm (Figure 1). If a result was abnormal, it was sent to 1 of 4 physician-reviewers who, using the electronic medical record, reviewed the discharge diagnosis; any related test results; and the discharge order, note, or summary (when available) to determine whether the result was potentially actionable. Any result mentioned in the discharge summary was excluded (these were most often final radiologic test results that did not differ from the preliminary results available to the inpatient team). Figure 1. Identifying results for physician review At both hospitals, the discharge order (including discharge diagnoses, medications, and follow-up appointments) was entered into the electronic medical record on the day of discharge and therefore was always available at the time of physician review. Of the 671 results that we reviewed, 525 (78%) were for patients who also had a dictated or typed discharge summary available at the time of review. When discharge summaries are completed after hospital discharge, inpatient physicians have access to the electronic medical record, including any test results that were not available on the day of discharge. The physician-reviewers are board-certified internists; 2 are hospitalists, and 2 are primary care physicians. If a physician-reviewer was involved in the care of a patient who had a result that required review, that result was sent to one of the other 3 reviewers. After reviewing the discharge order, the discharge summary, and related test results, the physician-reviewer used clinical judgment to determine whether the result required clinical action on the basis of the available information. A result was considered potentially actionable if it could change the management of the patient by requiring a new treatment or diagnostic test (or repeated testing), modification or discontinuation of a treatment or diagnostic testing, scheduling of an earlier follow-up appointment, or referral of the patient to another physician or specialist. The reviewer rated the result as definitely actionable, probably actionable, probably not actionable, or definitely not actionable. The reviewer also rated the urgency of the required action according to how soon it should occur: within 1 hour, 8 hours, 24 hours, 72 hours, 1 week, or 1 month. Surveys If the physician-reviewer defined a result as definitely actionable or probably actionable, either the inpatient physician or the primary care physician was surveyed by e-mail to determine whether he or she was aware of the result. At hospital A, the attending hospitalist was the inpatient physician surveyed; on the teaching service at hospital B, the junior resident was surveyed. On the nonhousestaff service at hospital B, the hospitalist or nonhospitalist attending physician was surveyed as the inpatient physician. The

Effect of bar-code technology on the safety of medication administration.

BACKGROUND Serious medication errors are common in hospitals and often occur during order transcription or administration of medication. To help prevent such errors, technology has been developed to verify medications by incorporating bar-code verification technology within an electronic medication-administration system (bar-code eMAR). METHODS We conducted a before-and-after, quasi-experimental study in an academic medical center that was implementing the bar-code eMAR. We assessed rates of errors in order transcription and medication administration on units before and after implementation of the bar-code eMAR. Errors that involved early or late administration of medications were classified as timing errors and all others as nontiming errors. Two clinicians reviewed the errors to determine their potential to harm patients and classified those that could be harmful as potential adverse drug events. RESULTS We observed 14,041 medication administrations and reviewed 3082 order transcriptions. Observers noted 776 nontiming errors in medication administration on units that did not use the bar-code eMAR (an 11.5% error rate) versus 495 such errors on units that did use it (a 6.8% error rate)--a 41.4% relative reduction in errors (P<0.001). The rate of potential adverse drug events (other than those associated with timing errors) fell from 3.1% without the use of the bar-code eMAR to 1.6% with its use, representing a 50.8% relative reduction (P<0.001). The rate of timing errors in medication administration fell by 27.3% (P<0.001), but the rate of potential adverse drug events associated with timing errors did not change significantly. Transcription errors occurred at a rate of 6.1% on units that did not use the bar-code eMAR but were completely eliminated on units that did use it. CONCLUSIONS Use of the bar-code eMAR substantially reduced the rate of errors in order transcription and in medication administration as well as potential adverse drug events, although it did not eliminate such errors. Our data show that the bar-code eMAR is an important intervention to improve medication safety. (ClinicalTrials.gov number, NCT00243373.)

Medication Dispensing Errors and Potential Adverse Drug Events before and after Implementing Bar Code Technology in the Pharmacy

Context Bar code technology could help reduce medication dispensing errors in the pharmacy. Contribution The authors observed hospital pharmacy technicians as they dispensed medications before and after the installation of a storage and retrieval system that used bar code technology to label medications. After implementation of the bar codebased system, dispensing errors were much less frequent if the system required scanning of all dispensed doses. Some errors actually increased if the system did not require scanning every dose. Cautions Bar code technology was only one part of an entirely redesigned medication storage and dispensing system. Implications Properly implemented, medication storage and dispensing systems that use bar code technology may help to reduce medication dispensing errors. The Editors Medication errors in hospitals are common (1, 2), and dispensing errors made in the pharmacy contribute considerably to these errors (3). Overall, dispensing error rates are relatively low, but because of the high volume of medications dispensed, more than 100 undetected dispensing errors may occur in a busy hospital pharmacy every day (4). Because only about one third of these dispensing errors are intercepted by nurses before medication administration (3), many errors reach hospitalized patients (5). Therefore, dispensing errors are an important target for patient safety interventions. Bar code technology has been touted as a promising strategy to prevent medication errors (6, 7). In industries outside of health care, bar code technology has been widely adopted because of its ease of use and high degree of reliability. In the context of pharmacy dispensing, if all medications in the pharmacy had a bar code that is scanned to ensure that the correct medication in its correct dose and formulation is being dispensed, dispensing errors may be substantially reduced. On the basis of the theoretical benefits for patient safety, the U.S. Food and Drug Administration (FDA) has mandated bar codes for all medications used in hospitals by April 2006 (8), and many institutions are beginning to adopt this technology to increase the accuracy of the dispensing and administration processes. Despite enthusiasm for this technology, few published studies have evaluated the effect of bar code technology on dispensing errors (9, 10). Previous work has also demonstrated that the implementation of health information technology (HIT) may result in unintended consequences and new types of errors (1113). Therefore, the decision to adopt this technology must be informed by a careful evaluation of its efficacy and limitations. To that end, we evaluated a recent implementation of bar code technology in a large hospital pharmacy to measure the changes in the rates of dispensing errors (see Glossary) and potential adverse drug events (ADEs) (see Glossary). Methods Study Site and Study Period We performed a before-and-after evaluation study over a 20-month period in a 735-bed tertiary care academic medical center, where approximately 5.9 million doses of medications were dispensed per year from the central inpatient pharmacy. Between February and August 2003 (prebar code implementation period), we measured the baseline rates of dispensing errors and potential ADEs. In November and December 2003, the hospital pharmacy converted to a bar codeassisted dispensing process. After the conversion, we remeasured the rates of dispensing errors and potential ADEs between May and September 2004 (postbar code implementation period). Observations in both periods were conducted on weekdays during the day shift, when most medications are dispensed. Dispensing Processes during Pre and PostBar Code Implementation Periods The Figure depicts an overview of the medication use process during the 2 observation periods. In both observation periods, the dispensing process involves 3 major steps that are commonly used in approximately 76% of U.S. hospitals (14) (Table 1 and Figure). In the first step, medications delivered to the pharmacy are stocked in the pharmacy inventory. The second step, known as filling, requires a pharmacy technician to retrieve the appropriate medications from the pharmacy inventory. The third step, known as verification, requires a staff pharmacist to verify the accuracy of the medications filled by the technician before delivery to patient care areas. If the staff pharmacist detects a dispensing error, the medication is returned for refilling. While the stocking and filling steps changed extensively with bar code technology implementation, the pharmacists visual inspection step remained functionally unchanged in the postbar code implementation period. In both periods, medications dispensed from the pharmacy would be delivered to either patient-specific medication drawers or semi-automated medication cabinets (Sure-Med, Omnicell, Mountain View, California) on the patient care units. Figure. Overview of the pharmacy dispensing process. *Sure-Med, Omnicell, Mountain View, California. CPOE = computerized physician order entry; MD = physician. Table 1. Description of the Dispensing Processes Studied in the PreBar Code and PostBar Code Implementation Periods In the prebar code implementation period, we studied 3 major dispensing processes: 1) Sure-Med fill, 2) first-dose fill, and 3) cart fill. Each medication dose (see Glossary) was dispensed by only 1 of these processes (Table 1). In the prebar code period, medications were stocked manually onto shelves and the filling step for all 3 processes was performed manually, with the pharmacy technician relying solely on visual inspection to pick the appropriate medication from the several storage areas in the pharmacy inventory. During the bar code conversion process, the study pharmacy built a dedicated repackaging center, which affixed a bar code onto every dose of medication (for example, each individual pill, vial, or ampoule) if the manufacturer had not applied a bar code. In the postbar code period, the prebar code dispensing processes were reorganized into 3 new dispensing processes: 1) carousel fill, 2) alternate zone fill, and 3) 2-day fill (Table 1). Each medication dose was dispensed by only 1 of these processes. For the 3 new dispensing processes, the pharmacy used a different configuration of bar codescanning technology to leverage a combination of internally developed and vendor-supplied software and hardware. Carousel Fill Process The carousel fill process dispensed the compact and nonrefrigeration-requiring forms of commonly used medications for the semi-automated medication cabinets (Sure-Med). These cabinets stored frequently used medications in medication-specific drawers, from which nurses dispensed doses for all patients on a particular unit. The Sure-Med fill process previously dispensed these medications. The new carousel fill process was so named because it used a newly purchased, bar codebased, high-volume storage and retrieval system called the carousel, which also monitored the supply levels in the Sure-Med cabinets to ensure an adequate supply of frequently used medications on each unit. When medications were stocked into the carousel, pharmacy staff scanned 1 dose per batch to ensure that the correct medications were placed in the appropriate compartment. When a pharmacy technician retrieved medications during the filling step, the machine directed the technician to the appropriate storage compartment within the carousel. The technician visually inspected the retrieved medication and scanned the bar code on it to ensure that he or she had retrieved the correct medication. In most cases, the carousel machine would instruct the technician to retrieve several doses of the same medication (a medication batch [see Glossary]) at a time to replenish the supplies for a particular cabinet. In these cases, only 1 dose was scanned. We will use Stock&Retrieve(+) Scan(+) as shorthand to characterize this process (see Glossary). Alternate Zone Process The alternate zone process dispensed commonly used medications that could not be accommodated in the carousel machine because of their size or need for refrigeration. Medications for this process were stocked onto shelves manually. When pharmacy technicians filled medications for this process, they manually retrieved the medications from the shelves, visually inspected them, and scanned their bar codes. Similar to the carousel fill process, if several doses of the same medication were being dispensed, only 1 dose was scanned. We will use Stock&Retrieve() Scan(+) as shorthand to characterize this process (see Glossary). Two-Day Fill Process The 2-day fill process handled less commonly used medications that the first-dose fill and cart fill processes previously dispensed to the patient-specific drawers on patient care units. Medications were stocked manually onto shelves and were retrieved by hand during the filling step. The technician in this process would typically retrieve several doses of the same medication at a time so that the patient-specific drawer in the patient care area would carry a 2-day supply. However, unlike the procedure in the carousel or alternate zone fill process, all doses retrieved in the 2-day fill process had to be scanned. We will use Stock&Retrieve() Scan(++) as shorthand to characterize this process (see Glossary). We excluded one dispensing process, controlled substance fill, which accounted for approximately 16% of daytime, weekday dispensing in the pharmacy, from the study because of limited research personnel and its lower baseline dispensing error rate (4). Measurement of Dispensing Error and Potential ADE Rates The primary outcomes of our study were the rates of target dispensing errors (see Glossary) and target potential ADEs (see Glossary). We used identical methods that were approved by the institutional review board at the study institution to measure the rates of dispensing errors in the prebar

Outpatient Prescribing Errors and the Impact of Computerized Prescribing

AbstractBACKGROUND: Medication errors are common among inpatients and many are preventable with computerized prescribing. Relatively little is known about outpatient prescribing errors or the impact of computerized prescribing in this setting. OBJECTIVE: To assess the rates, types, and severity of outpatient prescribing errors and understand the potential impact of computerized prescribing. DESIGN: Prospective cohort study in 4 adult primary care practices in Boston using prescription review, patient survey, and chart review to identify medication errors, potential adverse drug events (ADEs) and preventable ADEs. PARTICIPANTS: Outpatients over age 18 who received a prescription from 24 participating physicians. RESULTS: We screened 1879 prescriptions from 1202 patients, and completed 661 surveys (response rate 55%). Of the prescriptions, 143 (7.6%; 95% confidence interval (CI) 6.4% to 8.8%) contained a prescribing error. Three errors led to preventable ADEs and 62 (43%; 3% of all prescriptions) had potential for patient injury (potential ADEs); I was potentially life-threatening (2%) and 15 were serious (24%). Errors in frequency (n=77, 54%) and dose (n=26, 18%) were common. The rates of medication errors and potential ADEs were not significantly different at basic computerized prescribing sites (4.3% vs 11.0%, P=.31; 2.6% vs 4.0%, P=.16) compared to handwritten sites. Advanced checks (including dose and frequency checking) could have prevented 95% of potential ADEs. CONCLUSIONS: Prescribing errors occurred in 7.6% of outpatient prescriptions and many could have harmed patients. Basic computerized prescribing systems may not be adequate to reduce errors. More advanced systems with dose and frequency checking are likely needed to prevent potentially harmful errors.

Effect of an Electronic Medication Reconciliation Application and Process Redesign on Potential Adverse Drug Events A Cluster-Randomized Trial

BACKGROUND Medication reconciliation at transitions in care is a national patient safety goal, but its effects on important patient outcomes require further evaluation. We sought to measure the impact of an information technology-based medication reconciliation intervention on medication discrepancies with potential for harm (potential adverse drug events [PADEs]). METHODS We performed a controlled trial, randomized by medical team, on general medical inpatient units at 2 academic hospitals from May to June 2006. We enrolled 322 patients admitted to 14 medical teams, for whom a medication history could be obtained before discharge. The intervention was a computerized medication reconciliation tool and process redesign involving physicians, nurses, and pharmacists. The main outcome was unintentional discrepancies between preadmission medications and admission or discharge medications that had potential for harm (PADEs). RESULTS Among 160 control patients, there were 230 PADEs (1.44 per patient), while among 162 intervention patients there were 170 PADEs (1.05 per patient) (adjusted relative risk [ARR], 0.72; 95% confidence interval [CI], 0.52-0.99). A significant benefit was found at hospital 1 (ARR, 0.60; 95% CI, 0.38-0.97) but not at hospital 2 (ARR, 0.87; 95% CI, 0.57-1.32) (P = .32 for test of effect modification). Hospitals differed in the extent of integration of the medication reconciliation tool into computerized provider order entry applications at discharge. CONCLUSIONS A computerized medication reconciliation tool and process redesign were associated with a decrease in unintentional medication discrepancies with potential for patient harm. Software integration issues are likely important for successful implementation of computerized medication reconciliation tools.

Design and implementation of a comprehensive outpatient results manager

Prior research has demonstrated that clinicians often fail to review and act upon outpatient test results in a timely and appropriate manner. To address this patient safety and quality of care issue, Partners Healthcare has developed a browser-based, provider-centric, comprehensive results management application to help clinic physicians review and act upon test results in a safe, reliable, and efficient manner. The application, called the Results Manager, incorporates extensive decision support features to classify the degree of abnormality for each result, presents guidelines to help clinicians manage abnormal results, allows clinicians to generate result letters to patients with predefined, context-sensitive templates and prompts physicians to set reminders for future testing. In this paper, we outline the design process and functionality of Results Manager. We also discuss its underlying architectural design, which revolves around a clinical event monitor and a rules engine, and the methodological challenges encountered in designing this application.

Relationship between use of electronic health record features and health care quality: results of a statewide survey.

Background:Electronic health records (EHRs) are widely viewed as useful tools for supporting the provision of high quality healthcare. However, evidence regarding their effectiveness for this purpose is mixed, and existing studies have generally considered EHR usage a binary factor and have not considered the availability and use of specific EHR features. Objective:To assess the relationship between the use of an EHR and the use of specific EHR features with quality of care. Research Design:A statewide mail survey of physicians in Massachusetts conducted in 2005. The results of the survey were linked with Healthcare Effectiveness Data and Information Set (HEDIS) quality measures, and generalized linear regression models were estimated to examine the associations between the use of EHRs and specific EHR features with quality measures, adjusting for physician practice characteristics. Subjects:A stratified random sample of 1884 licensed physicians in Massachusetts, 1345 of whom responded. Of these, 507 had HEDIS measures available and were included in the analysis (measures are only available for primary care providers). Measure:Performance on HEDIS quality measures. Results:The survey had a response rate of 71%. There was no statistically significant association between use of an EHR as a binary factor and performance on any of the HEDIS measure groups. However, there were statistically significant associations between the use of many, but not all, specific EHR features and HEDIS measure group scores. The associations were strongest for the problem list, visit note and radiology test result EHR features and for quality measures relating to womens health, colon cancer screening, and cancer prevention. For example, users of problem list functionality performed better on womens health, depression, colon cancer screening, and cancer prevention measures, with problem list users outperforming nonusers by 3.3% to 9.6% points on HEDIS measure group scores (all significant at the P < 0.05 level). However, these associations were not universal. Conclusions:Consistent with past studies, there was no significant relationship between use of EHR as a binary factor and performance on quality measures. However, availability and use of specific EHR features by primary care physicians was associated with higher performance on certain quality measures. These results suggest that, to maximize health care quality, developers, implementers and certifiers of EHRs should focus on increasing the adoption of robust EHR systems and increasing the use of specific features rather than simply aiming to deploy an EHR regardless of functionality.

Errors associated with outpatient computerized prescribing systems

OBJECTIVE To report the frequency, types, and causes of errors associated with outpatient computer-generated prescriptions, and to develop a framework to classify these errors to determine which strategies have greatest potential for preventing them. MATERIALS AND METHODS This is a retrospective cohort study of 3850 computer-generated prescriptions received by a commercial outpatient pharmacy chain across three states over 4 weeks in 2008. A clinician panel reviewed the prescriptions using a previously described method to identify and classify medication errors. Primary outcomes were the incidence of medication errors; potential adverse drug events, defined as errors with potential for harm; and rate of prescribing errors by error type and by prescribing system. RESULTS Of 3850 prescriptions, 452 (11.7%) contained 466 total errors, of which 163 (35.0%) were considered potential adverse drug events. Error rates varied by computerized prescribing system, from 5.1% to 37.5%. The most common error was omitted information (60.7% of all errors). DISCUSSION About one in 10 computer-generated prescriptions included at least one error, of which a third had potential for harm. This is consistent with the literature on manual handwritten prescription error rates. The number, type, and severity of errors varied by computerized prescribing system, suggesting that some systems may be better at preventing errors than others. CONCLUSIONS Implementing a computerized prescribing system without comprehensive functionality and processes in place to ensure meaningful system use does not decrease medication errors. The authors offer targeted recommendations on improving computerized prescribing systems to prevent errors.

Quantifying Nursing Workflow in Medication Administration

New medication administration systems are showing promise in improving patient safety at the point of care, but adoption of these systems requires significant changes in nursing workflow. To prepare for these changes, the authors report on a time-motion study that measured the proportion of time that nurses spend on various patient care activities, focusing on medication administration-related activities. Implications of their findings are discussed.

Primary care physician attitudes concerning follow-up of abnormal test results and ambulatory decision support systems

OBJECTIVES Failures to follow-up abnormal test results are common in ambulatory care. Information systems could assist providers with abnormal test result tracking, yet little is known about primary care providers attitudes toward outpatient decision support systems. METHODS A cross-sectional survey of 216 primary care physicians (PCPs) that utilize a single electronic medical record (EMR) without computer-based clinical decision support. RESULTS The overall response rate was 65% (140/216). Less than one-third of the respondents were satisfied with their current system to manage abnormal laboratory, radiographs, Pap smear, or mammograms results. Only 15% of providers were satisfied with their system to notify patients of abnormal results. Over 90% of respondents felt automated systems to track abnormal test results would be useful. Seventy-nine percent of our respondents believed that they could comply better with guidelines through electronic clinical reminders. CONCLUSIONS Most PCPs were not satisfied with their methods for tracking abnormal results. Respondents believed that clinical decision support systems (CDSS) would be useful and could improve their ability to track abnormal results.