Improving Cancer Diagnosis and Care: Patient Access to Oncologic Imaging and Pathology Expertise and Technologies: Proceedings of a Workshop by unknow

Author:unknow
Language: eng
Format: epub
Tags: Health and Medicine: Cancer, Health and Medicine: Healthcare and Quality
Publisher: The National Academies Press
Published: 2018-09-11T00:00:00+00:00

FIGURE 1 Interoperability of health information systems for active clinical decision support.

NOTE: CCD = continuity of care document; CDA = clinical document architecture.

SOURCE: Created by David Chou, Department of Laboratory Medicine, University of Washington; provided for Shirts presentation, February 12, 2018.

Shirts pointed out that creating and maintaining a clinical decision support system can be extremely costly. These costs include the work of committees deciding on the clinical decision support rules, the alerts that should be included, and how the system should be designed; the time and effort to adapt the system based on pilot testing and clinician feedback; and costs to make the decision support system interoperable. “The costs of building and maintaining clinical decision support networks are often ignored or minimized; however, these costs can be substantial, especially if clinical decision support is implemented independently at each health care institution,” Shirts said.

For a genomic clinical decision support system at the University of Washington, Shirts said the estimated cost of communicating genomic information support to a clinician at the point of care was $4,600 per alert generated (Mathias et al., 2016). Shirts added that computer maintenance and system updates were expected to add 20 percent to the cost of developing the initial system per year that the system was operational, because maintenance can involve completely rebuilding the system every 3 or 4 years, or completely rebuilding decision support rule libraries to keep these interoperable with other systems. “It’s very challenging to keep systems connected with each other and to keep them up to date with current guidelines,” Shirts said. Every time a new guideline comes out, it must be translated into a computable language. “It would be wonderful if the guidelines could come out in a machine-readable format,” he added (IOM, 2015).

Shirts said that dramatic improvements in data interoperability and interinstitution collaboration will be necessary to drive decision support costs down (Mathias et al., 2016). In order to increase efficiency and reduce cost to less than $100 per alert, “we need to spread our efforts across multiple institutions in a collaborative effort to build these clinical decision support rules.” He suggested creating and following best practices for clinical decision support systems so that these tools can be disseminated quickly across multiple institutions (IOM, 2015).

Khorasani added that Harvard Medical School has created a public repository of evidence for clinical decision support that is machine readable, transparently graded, and continuously updated.25 Categories of evidence in the repository include clinical decision rules, professional society guidelines, and local best practices. He said Harvard curates and grades the available evidence in order to promote collaboration to accelerate the development of evidence-based clinical decision support tools.

Laser asked how long it should take for new clinical practice guidelines to be embedded in clinical decision support systems. Khorasani responded that for guidelines relating to imaging, vendors of imaging clinical decision support systems will need to update their tools within 1 year, based on a provision in the Protecting Access to Medicare Act.26 This law stipulates that starting in

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