Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 1307-1323
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 30-46 ◽  
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

scholarly journals Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3296
Author(s):  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Taqdir Ali ◽  
Syed Imran Ali ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Machine Learning Algorithms ◽  
Nominal Group ◽  
Specific Information ◽  
Matching Techniques ◽  
Disease Specific

Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns “Heuristic Patterns” using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinusitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians’ performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation.

Improving the Care of Patients With Regard to Chemotherapy-Induced Nausea and Emesis: The Effect of Feedback to Clinicians on Adherence to Antiemetic Prescribing Guidelines

2003 ◽  
Vol 21 (7) ◽  
pp. 1373-1378 ◽  
Author(s):  
Wilson C. Mertens ◽  
Donald J. Higby ◽  
David Brown ◽  
Regina Parisi ◽  
Janice Fitzgerald ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Guideline Development ◽  
Outcome Measurement ◽  
Statistical Process ◽  
Chemotherapy Administration ◽  
Physician Participation ◽  
Delayed Nausea ◽  
Nausea And Emesis

Purpose: To evaluate the effect of performance and outcomes feedback on adherence to clinical practice guidelines regarding chemotherapy-induced nausea and emesis (CINE). Methods: Institutional CINE clinical practice guidelines were developed based on American Society of Clinical Oncology guidelines. Consecutive administrations of moderately/highly emetogenic chemotherapy were assessed for errors. Baseline statistical process control (SPC) charts were created and mean errors per administration were calculated. Prospective SPC charts were used to measure the effect of guideline development and distribution, a visiting lecturer, and ongoing feedback regarding compliance with guidelines employing SPC charts. Patients were surveyed regarding the extent and severity of CINE for 5 days postadministration. These outcomes were then shared with physicians. Results: Baseline compliance was poor (mean, 0.87 omissions per chemotherapy administration), largely because of inadequate adherence to recommendations for delayed CINE management. Most patients experienced delayed nausea, particularly on day 3 postchemotherapy. Physician prescribing performance did not undergo sustained improvement despite guideline development or distribution, a lecture by a visiting expert, or sharing of adherence data with clinicians. Once patient outcomes were shared, physicians accepted the need for compliance and instituted nurse practitioner antiemetic prescribing, with almost complete compliance and concurrent measurable reduction in day 3 nausea. SPC charts documented improvements in both outcomes. Conclusions: SPC charts effectively monitor ongoing compliance and patient symptoms and represent appropriate outcome measurement and change facilitation tools. However, physician participation in guideline development and evidence of poor compliance alone did not improve prescribing performance. Only evidence of patient CINE experience coupled with noncompliance improved results.

An Analytics Framework for Physician Adherence to Clinical Practice Guidelines: Knowledge-Based Approach (Preprint)

2018 ◽  
Author(s):  
Jaehoon Lee ◽  
Nathan C Hulse
Keyword(s):  
Clinical Practice ◽  
Knowledge Base ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Models ◽  
Care Process ◽  
Knowledge Based ◽  
Clinical Databases ◽  
Knowledge Content

BACKGROUND One of the problems in evaluating clinical practice guidelines (CPGs) is the occurrence of knowledge gaps. These gaps may occur when evaluation logics and definitions in analytics pipelines are translated differently. OBJECTIVE The objective of this paper is to develop a systematic method that will fill in the cognitive and computational gaps of CPG knowledge components in analytics pipelines. METHODS We used locally developed CPGs that resulted in care process models (CPMs). We derived adherence definitions from the CPMs, transformed them into computationally executable queries, and deployed them into an enterprise knowledge base that specializes in managing clinical knowledge content. We developed a visual analytics framework, whose data pipelines are connected to queries in the knowledge base, to automate the extraction of data from clinical databases and calculation of evaluation metrics. RESULTS In this pilot study, we implemented 21 CPMs within the proposed framework, which is connected to an enterprise data warehouse (EDW) as a data source. We built a Web–based dashboard for monitoring and evaluating adherence to the CPMs. The dashboard ran for 18 months during which CPM adherence definitions were updated a number of times. CONCLUSIONS The proposed framework was demonstrated to accommodate complicated knowledge management for CPM adherence evaluation in analytics pipelines using a knowledge base. At the same time, knowledge consistency and computational efficiency were maintained.

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