Dataset Information

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance.

ABSTRACT: Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR).Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines.After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/MayoNlpPapCdss.We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

SUBMITTER: Ravikumar KE

PROVIDER: S-EPMC5801884 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance.

Ravikumar K E KE MacLaughlin Kathy L KL Scheitel Marianne R MR Kessler Maya M Wagholikar Kavishwar B KB Liu Hongfang H Chaudhry Rajeev R

Applied clinical informatics 20180124 1

<h4>Background</h4>Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results l ...[more]

PMID: 29365341

Similar Datasets

Project description:BACKGROUND:A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making. RESULTS:U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10?years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett's esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78-86% of eventual cases in the US population. CONCLUSIONS:This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates "what-if" scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed.

Project description:BackgroundAustralia has a comparatively high incidence of colorectal (bowel) cancer; however, population screening uptake using faecal occult blood test (FOBT) remains low. This study will determine the impact on screening participation of a novel, Internet-based Personalised Decision Support (PDS) package. The PDS is designed to measure attitudes and cognitive concerns and provide people with individually tailored information, in real time, that will assist them with making a decision to screen. The hypothesis is that exposure to (tailored) PDS will result in greater participation in screening than participation following exposure to non-tailored PDS or resulting from the current non-tailored, paper-based approach.Methods/designA randomised parallel trial comprising three arms will be conducted. Men and women aged 50-74 years (N = 3240) will be recruited. They must have access to the Internet; have not had an FOBT within the previous 12 months, or sigmoidoscopy or colonoscopy within the previous 5 years; have had no clinical diagnosis of bowel cancer. Groups 1 and 2 (PDS arms) will access a website and complete a baseline survey measuring decision-to-screen stage, attitudes and cognitive concerns and will receive immediate feedback; Group 1 will receive information 'tailored' to their responses in the baseline survey and group 2 will received 'non-tailored' bowel cancer information. Respondents in both groups will subsequently receive an FOBT kit. Group 3 (usual practice arm) will complete a paper-based version of the baseline survey and respondents will subsequently receive 'non-tailored' paper-based bowel cancer information with accompanying FOBT kit. Following despatch of FOBTs, all respondents will be requested to complete an endpoint survey. Main outcome measures are (1) completion of FOBT and (2) change in decision-to-screen stage. Secondary outcomes include satisfaction with decision and change in attitudinal scores from baseline to endpoint. Analyses will be performed using Chi-square tests, analysis of variance and log binomial generalized linear models as appropriate.DiscussionIt is necessary to restrict participants to Internet users to provide an appropriately controlled evaluation of PDS. Once efficacy of the approach has been established, it will be important to evaluate effectiveness in the wider at-risk population, and to identify barriers to its implementation in those settings.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12610000095066.

Project description:BackgroundElectronic medical records are widely used in family practices across Canada and can improve health outcomes. However, recent reports indicate that physicians using electronic medical records work longer and have less direct patient contact which may contribute to burnout. Therefore, new and innovative digital tools are essential to reduce physician workloads and improve patient-physician interaction to address physician burnout. The objective of this study was to assess the efficiency and accuracy of clinical decision-making when using a new preventive care point-of-care clinical decision support system (CDSS). An estimate of the potential annual time savings was also determined. This study also assessed physician reported perceived usefulness and ease of use of the CDSS.MethodsQuantitative and qualitative data were collected during this study. Each participant evaluated two simulated patient charts and identified which preventive care metrics were due. The participants recorded their decisions and the time required to assess each chart. Participants then completed a Technology Acceptance Model survey regarding the perceived usefulness and ease of use of the CDSS, which included qualitative feedback. The amount of time saved was determined and participants' clinical decision-making accuracy was scored against current Canadian preventive care guidelines. The number of preventive care specific visits completed per year was determined using clinic billing data.ResultsThe preventive care CDSS saved an average of 195.7 s of chart review time (249.5 s vs 445.2 s; P < 0.001). A total of 1520 preventive visits were performed at Primrose and Bruyère Family Medicine Centres. Extrapolated across the organization, implementation of the new tool could save 82.6 h per year. Decision-making accuracy was not affected by the new tool (78.4% vs 80.9%, P > 0.05). Participants rated the perceived ease of use and usefulness to be very high.ConclusionsNew digital tools may reduce providers' workload without impacting clinical decision-making accuracy. Participants indicated that the preventive care CDSS was useful and easy to use. Further software development and clinical studies are required to further improve and characterize the effect this new CDSS has when implemented in clinical practice.

Dataset Information

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance.

Publications

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets