Understanding implementation processes of clinical pathways and clinical practice guidelines in pediatric contexts: a study protocol

We presented a study protocol of developing Chinese medicine clinical practice guidelines for three common diseases in Hong Kong, including insomnia, chronic gastritis, and cerebral infarction. This research project will be conducted in three phases. First phase is the preparation stage which consists of the establishment of steering committee and panel. Second phase involves 6 steps, which are searching and identifying evidence, text mining process, Delphi survey, synthesizing of data, consensus conference, and drafting guidelines. In this phase, text mining technique, evidence-based method, and formal consensus method are combined to get consolidated supporting data as the source of CM CPGs. The final phase comprised external reviews, dissemination, and updating. The outputs from this project will provide three CM CPGs for insomnia, chronic gastritis, and cerebral infarction for Hong Kong local use.

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Transitioning Clinical Practice Guidelines into the Electronic Health Record through Clinical Pathways

Methods in Molecular Biology - Translational Bioinformatics for Therapeutic Development ◽

10.1007/978-1-0716-0849-4_4 ◽

2020 ◽

pp. 45-59

Author(s):

Sharjeel M. Hooda ◽

Karen K. Fields

Keyword(s):

Clinical Practice ◽

Electronic Health Record ◽

Clinical Practice Guidelines ◽

Practice Guidelines ◽

Clinical Pathways ◽

Health Record ◽

Electronic Health

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Data-Driven Approaches for Developing Clinical Practice Guidelines

Medical Education and Ethics ◽

10.4018/978-1-5225-0978-3.ch059 ◽

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.

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Data-Driven Approaches for Developing Clinical Practice Guidelines

Handbook of Research on Healthcare Administration and Management - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-5225-0920-2.ch003 ◽

2017 ◽

pp. 30-46 ◽

Cited By ~ 1

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.

Download Full-text