Yuko Kawasumi

The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review

A systematic review of the literature was performed to examine the impact of electronic health records (EHRs) on documentation time of physicians and nurses and to identify factors that may explain efficiency differences across studies. In total, 23 papers met our inclusion criteria; five were randomized controlled trials, six were posttest control studies, and 12 were one-group pretest-posttest designs. Most studies (58%) collected data using a time and motion methodology in comparison to work sampling (33%) and self-report/survey methods (8%). A weighted average approach was used to combine results from the studies. The use of bedside terminals and central station desktops saved nurses, respectively, 24.5% and 23.5% of their overall time spent documenting during a shift. Using bedside or point-of-care systems increased documentation time of physicians by 17.5%. In comparison, the use of central station desktops for computerized provider order entry (CPOE) was found to be inefficient, increasing the work time from 98.1% to 328.6% of physicians time per working shift (weighted average of CPOE-oriented studies, 238.4%). Studies that conducted their evaluation process relatively soon after implementation of the EHR tended to demonstrate a reduction in documentation time in comparison to the increases observed with those that had a longer time period between implementation and the evaluation process. This review highlighted that a goal of decreased documentation time in an EHR project is not likely to be realized. It also identified how the selection of bedside or central station desktop EHRs may influence documentation time for the two main user groups, physicians and nurses.

A Randomized Trial of the Effectiveness of On-demand versus Computer-triggered Drug Decision Support in Primary Care

OBJECTIVES Prescribing alerts generated by computerized drug decision support (CDDS) may prevent drug-related morbidity. However, the vast majority of alerts are ignored because of clinical irrelevance. The ability to customize commercial alert systems should improve physician acceptance because the physician can select the circumstances and types of drug alerts that are viewed. We tested the effectiveness of two approaches to medication alert customization to reduce prevalence of prescribing problems: on-physician-demand versus computer-triggered decision support. Physicians in each study condition were able to preset levels that triggered alerts. DESIGN This was a cluster trial with 28 primary care physicians randomized to either automated or on-demand CDDS in the MOXXI drug management system for 3,449 of their patients seen over the next 6 months. MEASUREMENTS The CDDS generated alerts for prescribing problems that could be customized by severity level. Prescribing problems included dosing errors, drug-drug, age, allergy, and disease interactions. Physicians randomized to on-demand activated the drug review when they considered it clinically relevant, whereas physicians randomized to computer-triggered decision support viewed all alerts for electronic prescriptions in accordance with the severity level they selected for both prevalent and incident problems. Data from administrative claims and MOXXI were used to measure the difference in the prevalence of prescribing problems at the end of follow-up. RESULTS During follow-up, 50% of the physicians receiving computer-triggered alerts modified the alert threshold (n = 7), and 21% of the physicians in the alert-on-demand group modified the alert level (n = 3). In the on-demand group 4,445 prescribing problems were identified, 41 (0.9%) were seen by requested drug review, and in 31 problems (75.6%) the prescription was revised. In comparison, 668 (10.3%) of the 6,505 prescribing problems in the computer-triggered group were seen, and 81 (12.1%) were revised. The majority of alerts were ignored because the benefit was judged greater than the risk, the interaction was known, or the interaction was considered clinically not important (computer-triggered: 75.8% of 585 ignored alerts; on-demand: 90% of 10 ignored alerts). At the end of follow-up, there was a significant reduction in therapeutic duplication problems in the computer-triggered group (odds ratio 0.55; p = 0.02) but no difference in the overall prevalence of prescribing problems. CONCLUSION Customization of computer-triggered alert systems is more useful in detecting and resolving prescribing problems than on-demand review, but neither approach was effective in reducing prescribing problems. New strategies are needed to maximize the use of drug decision support systems to reduce drug-related morbidity.

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

OBJECTIVE To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases. STUDY SETTING An existing electronic medical record project in Montreal, Quebec. STUDY DESIGN One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physicians electronic medical record. DATA COLLECTION/EXTRACTION METHODS Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample. PRINCIPAL FINDINGS The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims-specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined. CONCLUSIONS Our algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.

Evaluation of the use of an integrated drug information system by primary care physicians for vulnerable population

OBJECTIVE To investigate whether an electronic prescribing and integrated drug information system was more likely to be used by primary care physicians for patients of low socioeconomic (SES) patients. METHODS Prospective 9 months follow-up study was conducted in Montreal, Canada from March to November 2003. The study included 28 primary care physicians and their 4096 respective patients with provincial drug insurance. Utilization rate was defined as the number of times the electronic medication history (EMH) and electronic prescribing system (E-rx) were accessed divided by the total number of medical visits made by those patients. System audit trails (utilization), provincial health insurance databases (visits) were used to measure system utilization rate. For each patient neighborhood-based measures of household income, derived from Statistics Canada, were used to measure socioeconomic status. RESULTS The EMH was used 14.5 times per 100 visits. In comparison to high SES patients, there was a significant 70% increase (RR: 1.70; 95%CI: 1.15-2.47) in the EMH utilization for low SES patients. The electronic prescribing system was used 38.5 times per 100 visits and did not vary by patient SES. The EMH utilization rate for low SES patients with multiple emergency room (ER) visits was 2.4 times higher than for high SES patients with <1 ER visit (RR: 2.38; 95%CI: 1.36-4.14). The utilization rate for low SES patients, who took, at least six drugs per day, was four times higher compared to high SES patients with less complex drug management (RR: 4.00; 95%CI: 2.22-7.17). CONCLUSIONS Primary care physicians were more likely to access electronic information on current drug use for patients of low SES taking multiple medications and with fragmented care.

Non-participation bias in health services research using data from an integrated electronic prescribing project: the role of informed consent

Electronic prescribing potentially reduces adverse outcomes and provides critical information for drug safety research but studies may be distorted by non-participation bias. 52,507 patients and 28 physicians were evaluated to determine characteristics associated with consent status in an electronic prescribing project. Physicians with less technology proficiency, seeing more patients, and having patients with higher fragmentation of care were less likely to obtain consent. Older patients with complex health status, higher income, and more visits to the study physician were more likely to consent. These systematic differences could result in significant non-participation bias for research conducted only with consenting patients.

SESGO PELA FALTA DE PARTICIPAÇÃO NA PESQUISA DOS SERVIÇOS DE SAÚDE AO USAR DADOS DE UM PROJETO INTEGRADO DE PRESCRIÇÃO ELETRÔNICA: O PAPEL DO CONSENTIMENTO INFORMADO

Electronic prescribing potentially reduces adverse outcomes and provides critical information for drug safety research but studies may be distorted by non-participation bias. 52,507 patients and 28 physicians were evaluated to determine characteristics associated with consent status in an electronic prescribing project. Physicians with less technology proficiency, seeing more patients, and having patients with higher fragmentation of care were less likely to obtain consent. Older patients with complex health status, higher income, and more visits to the study physician were more likely to consent. These systematic differences could result in significant non-participation bias for research conducted only with consenting patients.

Sesgo por falta de participación en la investigación de los servicios de salud al usar datos de un proyecto integrado de prescripción electrónica: el papel del consentimiento informado

Electronic prescribing potentially reduces adverse outcomes and provides critical information for drug safety research but studies may be distorted by non-participation bias. 52,507 patients and 28 physicians were evaluated to determine characteristics associated with consent status in an electronic prescribing project. Physicians with less technology proficiency, seeing more patients, and having patients with higher fragmentation of care were less likely to obtain consent. Older patients with complex health status, higher income, and more visits to the study physician were more likely to consent. These systematic differences could result in significant non-participation bias for research conducted only with consenting patients.