Existing interventions including improving communication and self-care to improve readmission of patients undergoing high risk colorectal surgery involving new ileostomy formation has shown limited results. Our proposal is to deploy a wearable solution that predicts physiological perturbation with continuous remote patient monitoring and advanced machine learning algorithms which will be connected to structured, cascading, escalation pathways and care coordination involving home health nurses, colorectal and ostomy nurses, and colorectal surgeons, and has the potential to transform surgical management in the post-discharge period, where patients are the most vulnerable for readmission. This feasibility study will contribute to the understanding of post-discharge continuous remote monitoring of ileostomy patients, promote patient self-care, and has the potential of improving patient outcomes.
Colorectal surgery is a high-risk surgery that results in significant morbidity, and health care utilization in the form of readmission. Ileostomy creation is a significant risk factor in colorectal surgery rehospitalization. Effective continuous remote patient monitoring (CRPM) can reduce readmissions, but it has only been realized in select heart failure populations via invasive monitoring. The investigators will focus on colorectal CRPM in the elective, new ileostomy population through a structured cascading and escalating alert system. In this feasibility study, the investigators will use a wearable biosensor and collect ambulatory physiological data that are analyzed by machine learning algorithms, to generate personalized alerts of physiological perturbation in colorectal surgery patients in the post-discharge period. Alerts from this algorithm may be cascaded with other patient status data to inform management by the home health team via a structured protocol built into the electronic health record (EHR). The escalation pathway will engage home health nurses, colorectal care team nurses, ostomy nurses, and colorectal surgeons. The investigators will conduct surveys and semi-structured interviews with patients, and semi-structured interviews with providers, which will be used to evaluate the perceptions, acceptance, and experience of this CRPM solution.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
11
Continuous patient monitoring through non-invasive biosensors coupled with machine learning algorithms, with a structured escalation and communication pathway for home health providers and colorectal clinical team
Surveys and interviews with enrolled participants
NorthShore University HealthSystem Evanston Hospital
Evanston, Illinois, United States
NorthShore University HealthSystem Glenbrook Hospital
Glenview, Illinois, United States
NorthShore University HealthSystem HighlandPark Hospital
Highland Park, Illinois, United States
Attrition Rate
Drop out from study
Time frame: 30 days from patient discharge date
Enrollment Rate
Enrollment rate for entire patient cohort
Time frame: Through study completion, an average of 30 days for each patient
30 Days Readmission
30-day readmission to hospital
Time frame: 30 days from patient discharge date
Number of Participants With Stool Regimen Escalation
Frequency of stool regimen escalation by providers
Time frame: 30 days from patient discharge date
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