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Clinical and Operational Insights from Data-driven Care Pathway Mapping: a Systematic Review

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Publisher Biomed Central
Date 2022 Feb 18
PMID 35177058
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Abstract

Background: Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The "as is" or "de facto" care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.

Methods: A systematic review was conducted of literature abstracting sequential patient care activities ("de facto" care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.

Results: 254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.

Discussion: Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.

Conclusion: This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

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References
1.
Alvarez C, Rojas E, Arias M, Munoz-Gama J, Sepulveda M, Herskovic V . Discovering role interaction models in the Emergency Room using Process Mining. J Biomed Inform. 2018; 78:60-77. DOI: 10.1016/j.jbi.2017.12.015. View

2.
Perer A, Wang F, Hu J . Mining and exploring care pathways from electronic medical records with visual analytics. J Biomed Inform. 2015; 56:369-78. DOI: 10.1016/j.jbi.2015.06.020. View

3.
Connell A, Montgomery H, Martin P, Nightingale C, Sadeghi-Alavijeh O, King D . Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions. NPJ Digit Med. 2019; 2:67. PMC: 6669220. DOI: 10.1038/s41746-019-0100-6. View

4.
Nuemi G, Afonso F, Roussot A, Billard L, Cottenet J, Combier E . Classification of hospital pathways in the management of cancer: application to lung cancer in the region of burgundy. Cancer Epidemiol. 2013; 37(5):688-96. DOI: 10.1016/j.canep.2013.06.007. View

5.
Rotter T, Kinsman L, James E, Machotta A, Gothe H, Willis J . Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst Rev. 2010; (3):CD006632. DOI: 10.1002/14651858.CD006632.pub2. View