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Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery

  • Kamal MaheshwariEmail author
  • Jacek Cywinski
  • Piyush Mathur
  • Kenneth C. CummingsIII
  • Rafi Avitsian
  • Timothy Crone
  • David Liska
  • Francis X. Campion
  • Kurt Ruetzler
  • Andrea Kurz
Original Research

Abstract

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting adoption and efficacy of clinical pathway. Machine intelligence can potentially identify clinical variation and may provide useful insights to create and optimize clinical pathways. In this quality improvement project we analyzed the inpatient care of 1786 patients undergoing colorectal surgery from 2015 to 2016 across multiple Ohio hospitals in the Cleveland Clinic System. Data from four information subsystems was loaded in the Clinical Variation Management (CVM) application (Ayasdi, Inc., Menlo Park, CA). The CVM application uses machine intelligence and topological data analysis methods to identify groups of similar patients based on the treatment received. We defined “favorable performance” as groups with lower direct variable cost, lower length of stay, and lower 30-day readmissions. The software auto-generated 9 distinct groups of patients based on similarity analysis. Overall, favorable performance was seen with ketorolac use, lower intra-operative fluid use (< 2000 cc) and surgery for cancer. Multiple sub-groups were easily created and analyzed. Adherence reporting tools were easy to use enabling almost real time monitoring. Machine intelligence provided useful insights to create and monitor care pathways with several advantages over traditional analytic approaches including: (1) analysis across disparate data sets, (2) unsupervised discovery, (3) speed and auto-generation of clinical pathways, (4) ease of use by team members, and (5) adherence reporting.

Keywords

Machine intelligence Clinical monitoring Clinical pathway 

Notes

Author Contributions

KM, JC, RA, TC: helped design the study, analyze the data, revise the article for scientific and intellectual content, and prepare the manuscript. PM, FXC: helped analyze the data, revise the article for scientific and intellectual content, and prepare the manuscript. DL, KR, AK, KCC: helped revise the article for scientific and intellectual content, and prepare the manuscript.

Compliance with ethical standards

Conflict of interest

Francis X. Campion served as Chief Medical Officer for Ayasdi Inc during this project. None of the other authors have a personal financial interest in this work.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Kamal Maheshwari
    • 1
    • 2
    Email author
  • Jacek Cywinski
    • 1
  • Piyush Mathur
    • 1
  • Kenneth C. CummingsIII
    • 1
  • Rafi Avitsian
    • 1
  • Timothy Crone
    • 3
  • David Liska
    • 4
  • Francis X. Campion
    • 5
  • Kurt Ruetzler
    • 1
    • 2
  • Andrea Kurz
    • 1
    • 2
  1. 1.Department of General Anesthesiology, Anesthesiology InstituteCleveland ClinicClevelandUSA
  2. 2.Departments of Outcomes Research, Anesthesiology InstituteCleveland ClinicClevelandUSA
  3. 3.Department of Hospital Medicine Cleveland ClinicClevelandUSA
  4. 4.Departments of Colorectal Surgery, Digestive Disease InstituteCleveland ClinicClevelandUSA
  5. 5.Ayasdi, IncMenlo ParkUSA

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