The Impact of Artificial Intelligence/Machine Learning (AI/ML) on Time to Palliative Care Review in an Inpatient Hospital Population
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- Mayo Clinic
- 入组人数
- 2,231
- 试验地点
- 2
- 主要终点
- Timely identification for need of palliative care
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19,
- •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.
排除标准
- •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.
- •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
结局指标
主要结局
Timely identification for need of palliative care
时间窗: 12 months
Measured as time in hours to the electronic record of consult by the palliative care team in the inpatient setting.
次要结局
- The number of inpatient palliative care consults(12 months)
- Transition time to hospice-designated bed(12 months)
- Rate of discharge to external hospice(12 months)
- Timely identification for need of palliative care per unit(12 months)
- Hospitalization or readmission within 30 days of discharge(12 months)
- ICU transfers(12 months)
- Time to hospice designation(12 months)
- Inpatient length of stay(12 months)
- Emergency Department visit within 30 days of discharge(12 months)
- Ratio of inpatient hospice death to non-hospice hospital deaths(12 months)