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Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases.

ABSTRACT: OBJECTIVES:To evaluate the potential effect of computer support on general practitioners' management of familial breast and ovarian cancer, and to compare the effectiveness of two different types of computer program. DESIGN:Crossover experiment with balanced block design. PARTICIPANTS:Of a random sample of 100 general practitioners from Buckinghamshire who were invited, 41 agreed to participate. From these, 36 were selected for a fully balanced study. INTERVENTIONS:Doctors managed 18 simulated cases: 6 with computerised decision support system Risk Assessment in Genetics (RAGs), 6 with Cyrillic (an established pedigree drawing program designed for clinical geneticists), and 6 with pen and paper. MAIN OUTCOME MEASURES:Number of appropriate management decisions made (maximum 6), mean time taken to reach a decision, number of pedigrees accurately drawn (maximum 6). Secondary measures were method of support preferred for particular aspects of managing family histories of cancer; importance of specific information on cancer genetics that might be provided by an "ideal computer program." RESULTS:RAGs resulted in significantly more appropriate management decisions (median 6) than either Cyrillic (median 3) or pen and paper (median 3); median difference between RAGs and Cyrillic 2.5 (95% confidence interval 2.0 to 3.0; P<0.0001). RAGs also resulted in significantly more accurate pedigrees (median 5) than both Cyrillic (median 3.5) and pen and paper (median 2); median difference between RAGs and Cyrillic 1.5 (1.0 to 2.0; P<0.0001). The time taken to use RAGs (median 178 seconds) was 51 seconds longer per case (95% confidence interval 36 to 65; P<0.0001) than pen and paper (median 124 seconds) but was less than Cyrillic (median 203 seconds; difference 23. (5 to 43; P=0.02)). 33 doctors (92% (78% to 98%)) preferred using RAGs overall. The most important elements of an "ideal computer program" for genetic advice in primary care were referral advice, the capacity to create pedigrees, and provision of evidence and explanations to support advice. CONCLUSIONS:RAGs could enable general practitioners to be more effective gatekeepers to genetics services, empowering them to reassure the majority of patients with a family history of breast and ovarian cancer who are not at increased genetic risk.

SUBMITTER: Emery J

PROVIDER: S-EPMC27423 | biostudies-literature | 2000 Jul

REPOSITORIES: biostudies-literature

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Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases.

Emery J J Walton R R Murphy M M Austoker J J Yudkin P P Chapman C C Coulson A A Glasspool D D Fox J J

BMJ (Clinical research ed.) 20000701 7252

<h4>Objectives</h4>To evaluate the potential effect of computer support on general practitioners' management of familial breast and ovarian cancer, and to compare the effectiveness of two different types of computer program.<h4>Design</h4>Crossover experiment with balanced block design.<h4>Participants</h4>Of a random sample of 100 general practitioners from Buckinghamshire who were invited, 41 agreed to participate. From these, 36 were selected for a fully balanced study.<h4>Interventions</h4>D ...[more]

PMID: 10875832

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Project description:OBJECTIVE: The aim of this study was to compare the clinical impact of computerized decision support with and without electronic access to clinical guidelines and laboratory data on antibiotic prescribing decisions. DESIGN: A crossover trial was conducted of four levels of computerized decision support-no support, antibiotic guidelines, laboratory reports, and laboratory reports plus a decision support system (DSS), randomly allocated to eight simulated clinical cases accessed by the Web. MEASUREMENTS: Rate of intervention adoption was measured by frequency of accessing information support, cost of use was measured by time taken to complete each case, and effectiveness of decision was measured by correctness of and self-reported confidence in individual prescribing decisions. Clinical impact score was measured by adoption rate and decision effectiveness. RESULTS: Thirty-one intensive care and infectious disease specialist physicians (ICPs and IDPs) participated in the study. Ventilator-associated pneumonia treatment guidelines were used in 24 (39%) of the 62 case scenarios for which they were available, microbiology reports in 36 (58%), and the DSS in 37 (60%). The use of all forms of information support did not affect clinicians' confidence in their decisions. Their use of the DSS plus microbiology report improved the agreement of decisions with those of an expert panel from 65% to 97% (p=0.0002), or to 67% (p=0.002) when antibiotic guidelines only were accessed. Significantly fewer IDPs than ICPs accessed information support in making treatment decisions. On average, it took 245 seconds to make a decision using the DSS compared with 113 seconds for unaided prescribing (p<0.001). The DSS plus microbiology reports had the highest clinical impact score (0.58), greater than that of electronic guidelines (0.26) and electronic laboratory reports (0.45). CONCLUSION: When used, computer-based decision support significantly improved decision quality. In measuring the impact of decision support systems, both their effectiveness in improving decisions and their likely rate of adoption in the clinical environment need to be considered. Clinicians chose to use antibiotic guidelines for one third and microbiology reports or the DSS for about two thirds of cases when they were available to assist their prescribing decisions.

Project description:Computer-based decision support systems are a promising method for incorporating research evidence into clinical practice. However, evidence is still scant on how such information technology solutions work in primary healthcare when support is provided across many health problems. In Finland, we designed a trial where a set of evidence-based, patient-specific reminders was introduced into the local Electronic Patient Record (EPR) system. The aim was to measure the effects of such reminders on patient care. The hypothesis was that the total number of triggered reminders would decrease in the intervention group compared with the control group, indicating an improvement in patient care.From July 2009 to October 2010 all the patients of one health center were randomized to an intervention or a control group. The intervention consisted of patient-specific reminders concerning 59 different health conditions triggered when the healthcare professional (HCP) opened and used the EPR. In the intervention group, the triggered reminders were shown to the HCP; in the control group, the triggered reminders were not shown. The primary outcome measure was the change in the number of reminders triggered over 12 months. We developed a unique data gathering method, the Repeated Study Virtual Health Check (RSVHC), and used Generalized Estimation Equations (GEE) for analysing the incidence rate ratio, which is a measure of the relative difference in percentage change in the numbers of reminders triggered in the intervention group and the control group.In total, 13,588 participants were randomized and included. Contrary to our expectation, the total number of reminders triggered increased in both the intervention and the control groups. The primary outcome measure did not show a significant difference between the groups. However, with the inclusion of patients followed up over only six months, the total number of reminders increased significantly less in the intervention group than in the control group when the confounding factors (age, gender, number of diagnoses and medications) were controlled for.Computerized, tailored reminders in primary care did not decrease during the 12 months of follow-up time after the introduction of a patient-specific decision support system.ClinicalTrial.gov NCT00915304.

Project description:BACKGROUND:It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15?years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the 'Fiebig staging' system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests - as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. METHODS:The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time 'point estimates' and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). RESULTS:In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. CONCLUSIONS:This tool, available at https://tools.incidence-estimation.org/idt/ , is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

Dataset Information

Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases.

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Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases.

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