Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.
J Clin Monit Comput
; 33(4): 725-731, 2019 Aug.
Article
in En
| MEDLINE
| ID: mdl-30251058
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Medical Informatics
/
Signal Processing, Computer-Assisted
/
Artificial Intelligence
/
Monitoring, Intraoperative
/
Colorectal Surgery
/
Colonic Neoplasms
Type of study:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Clin Monit Comput
Journal subject:
INFORMATICA MEDICA
/
MEDICINA
Year:
2019
Document type:
Article
Affiliation country:
United States