Clinical trial NCT03976297: Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population
Official title: The Impact of Artificial Intelligence/Machine Learning (AI/ML) on Time to Palliative Care Review in an Inpatient Hospital Population
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Palliative Care
Keywords: Machine learning
Artificial Intelligence
Pragmatic Clinical Trial
Healthcare Delivery
Lead sponsor: Mayo Clinic
Sponsor class: OTHER
First posted: 2019-06-06
Enrollment: 2231
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Intervention model description: Pragmatic Stepped Wedge Cluster Randomized Trial
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.

Detailed description: The need for timely palliative care is crucial. Aging patient populations are becoming more complex, often needing care from multiple specialties. There has been a growing mismatch between clinical care and patient preferences particularly with regards to services near end-of-life. Research has shown that that most people prefer to die at home despite the majority dying outside of the home (nursing home or hospital). Given the current model of care and incentives palliative care is considered the care of last resort after all attempts at cure have been exhausted. This delay can lead to sub-optimal symptom management for pain and lower quality of life. As the demand for palliative care increases, policy initiatives and referral triage tools to that lead to quality palliative care services are needed.

In 2018 the Mayo Clinic developed a fully integrated information technology (IT) solution focusing on the identification of patients who may benefit from early palliative care review. The tool, known as Control Tower, pulls disparate data sources centered on a machine learning algorithm which predicts the need for palliative care in hospital. This algorithm was put into production as of December 2018 into a silent mode. The algorithm along with other key patient indicators are integrated into a graphical user interface (GUI) which allows a human operator to review the algorithm predictions and subsequently record the operator's assessment. The tool is expected to enhance risk assessment and create a healthcare model in which palliative care can pro-actively and effectively screen for patient need. Anticipated benefits of the approach include improved symptom control and patient satisfaction as well as a measurable impact on inpatient hospital mortality.

The overall objective of this study is to assess the effectiveness and implementation of the Control Tower palliative care algorithm into hospital practice by creating a stepped wedge cluster randomized trial in 16 inpatient units. By creating an algorithm that automatically screens and monitors patient health status during inpatient hospitalization, the investigators hypothesize that participants will receive needed palliative care earlier than under the usual course of care. In addition to testing clinical effectiveness study members will also collect data for process measures to assess the algorithm and healthcare performance after translation of the prediction algorithm from a research domain to a practice setting.

Eligibility criteria: Inclusion Criteria:

* Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19, 2020.
* Once a day Monday through Friday, the CT operator selects 12 patients from all of the nursing units that are participating in the trial (whether or not they are currently in the intervention group) with palliative scores of at least 7 (out of 100), i.e., those that are high risk and displayed as red in the CT GUI (unless they are already being seen by palliative care.)
* The CT operator chooses the selected patients by looking at the patients in sorted order starting with the highest score and proceeding down the list, evaluating each patient for exclusion criteria.
* Once the CT operator identifies 12 appropriate patients or once they reaches the end of the high-risk patients (score of 7 or higher) they stop.

Exclusion Criteria:

* We will exclude all patients who do not provide research authorization to review their medical records for general research studies in accordance with Minnesota Statute 144.335.
* We will exclude patients under the age of 18 years of age.
* We will exclude patients previously seen by Palliative care during the index hospital visit (i.e., green icon within CT user interface regardless of score)
* We will exclude patient who no longer have an active encounter (patients who have died or patients who have transferred to another facility are excluded) at the time of the review
* We will exclude patients currently enrolled with the Hospice service at Mayo
* We will exclude patients currently enrolled in the Palliative Homebound program (an alternative healthcare model at Mayo)
* We will exclude patients who are about to be discharged in the next 24 hours through indication of note

Primary outcomes:
measure: Timely identification for need of palliative care
timeFrame: 12 months
Secondary outcomes:
measure: The number of inpatient palliative care consults
timeFrame: 12 months
measure: Timely identification for need of palliative care per unit
timeFrame: 12 months
measure: Transition time to hospice-designated bed
timeFrame: 12 months
measure: Time to hospice designation
timeFrame: 12 months
measure: Emergency Department visit within 30 days of discharge
timeFrame: 12 months
measure: Hospitalization or readmission within 30 days of discharge
timeFrame: 12 months
measure: ICU transfers
timeFrame: 12 months
measure: Ratio of inpatient hospice death to non-hospice hospital deaths
timeFrame: 12 months
measure: Rate of discharge to external hospice
timeFrame: 12 months
measure: Inpatient length of stay
timeFrame: 12 months
Other outcomes:
Locations:
city: Rochester
country: United States
facility: Mayo Clinic
state: Minnesota
Study officials:
affiliation: Mayo Clinic
name: Jon O Ebbert, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Wilson PM, Philpot LM, Ramar P, Storlie CB, Strand J, Morgan AA, Asai SW, Ebbert JO, Herasevich VD, Soleimani J, Pickering BW. Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial. Trials. 2021 Sep 16;22(1):635. doi: 10.1186/s13063-021-05546-5.