Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 674
- 试验地点
- 1
- 主要终点
- Composite patient-level outcomes related to opioids
研究概览
简要总结
This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
详细描述
This clinical trial evaluates the pilot implementation of a ML-driven CDS tool designed to predict opioid overdose risk within the electronic health record (EHR) system at thirteen UF Health internal medicine and family medicine clinics in Gainesville, Florida.
The implementation process involved backend and frontend development and integration of the CDS tool. For backend integration, the investigators reviewed clinical workflows, designed a data flow plan to incorporate risk scores into patient charts, and collaborated with UF Health IT and Integrated Data Repository (IDR) Research Services to address alert implementation, data flow, server specifications, and responsibilities. Risk assessments approved by UF Health IT and the institutional review board (IRB) ensured secure access to patient health information (PHI) and enabled EHR integration. For frontend development, the investigators used a user-centered design approach to create the CDS tool prototype, incorporating feedback from PCPs during formative interviews to refine the user interface and ensure timely, actionable alerts through the EPIC system without disrupting clinical workflows.
The study primarily aims to assess the usability, acceptance, and feasibility of the CDS tool six months post-implementation through mixed-method evaluations. Researchers will use semi-structured interviews and an online questionnaire to collect feedback from PCPs, focusing on alert usability, preferences, and outcomes. Quantitative analyses will evaluate alert penetration, usage patterns, and PCP actions, while qualitative analyses will explore themes and insights from override comments to guide tool optimization. Researchers will also explore secondary patient-level outcomes using EHR data such as naloxone prescriptions.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 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
- •For PCP level outcomes assessment
- •practicing in any of the 13 participating clinics (10 UF Health Family Medicine clinics and 3 UF Health Internal Medicine) in Gainesville, Florida.
- •For patient level outcomes assessment:
- •Inclusion criteria: Patients who seen in any of the 9 participating UF Health clinics who
- •are aged ≥18 years
- •received any opioid prescription in the past year prior to their clinic visit.
- •are identified as being at elevated risk for overdose by the ML algorithm.
排除标准
- •Patients who
- •had malignant cancer diagnosis or hospice care prior to study enrollment
研究组 & 干预措施
Overdose Prevention Alert (OPA) Intervention Arm
The intervention arm will receive a ML CDS tool that provides interruptive alerts for patients at elevated risk of opioid overdose, triggered when a clinician signs an opioid order.
干预措施: Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention (Behavioral)
结局指标
主要结局
Composite patient-level outcomes related to opioids
时间窗: From enrollment and up to 12 months (3, 6, 12 months) post implementation of the OPA
The CDS tool will generate an Overdose Prevention Alert (OPA) when a PCP signs an opioid order in Epic®. To evaluate the tool's effectiveness, researchers will conduct within-clinic comparisons (pre- vs. post-implementation) and examine a composite of patient-level outcomes post-implementation, including the proportion of patients having any of the following 6 outcomes: 1. receipt of a naloxone order or prescription fill; 2. absence of opioid overdose diagnoses and naloxone administration; 3. absence of ED visits or hospitalizations due to opioid overdose or OUD; 4. absence of overlapping opioid and benzodiazepine use; 5. absence of high-dose opioid use (average daily morphine milligram equivalent ≥50); 6. receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care).
PCP's use feedback of the Overdose Prevention Alert (OPA)
时间窗: From enrollment and up to 7 months post implementation of the OPA
An online questionnaire for PCPs who interacted with OPA includes 12 Likert-scale items (4-point scale: 1 = Strongly Disagree to 4 = Strongly Agree) assessing OPA's acceptability, appropriateness, and feasibility: 1. OPA's information was clear. 2. OPA was easy to use. 3. OPA helps identify patients at increased overdose risk. 4. OPA helps understand patient's overdose risk. 5. OPA provides risk management recommendations. 6. OPA identifies the right patients with elevated overdose risk. 7. OPA notifies the correct healthcare team member (i.e., PCPs). 8. A pop-up alert is an appropriate notification approach. 9. Signing an opioid order is the right time for OPA. 10. Alert frequency is appropriate. 11. I prefer OPA over the legacy naloxone alert (see picture). 12. I want this OPA to continue to operate in my EHR. Mean scores (with standard deviations \[SD\]) will be calculated across all items, as well as individual average scores (SD).
次要结局
- Receipt of a naloxone order or prescription fill(From enrollment and up to 12 months (3, 6, 12 months) post implementation of the Overdose Prevention Alert (OPA))
- Absence of opioid overdose diagnoses and naloxone administration(From enrollment and up to 12 months (3, 6, 12 months) post implementation)
- Absence of ED visits or hospitalizations due to opioid overdose or OUD(From enrollment and up to 12 months (3, 6, 12 months) post implementation)
- Absence of overlapping opioid and benzodiazepine use(From enrollment and up to 12 months (3, 6, 12 months) post implementation)
- Absence of high-dose opioid use (average daily morphine milligram equivalent ≥50)(From enrollment and up to 12 months (3, 6, 12 months) post implementation)
- Receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care(From enrollment and up to 12 months (3, 6, 12 months) post implementation)
研究者
Wei-Hsuan Lo-Ciganic
Professor
University of Pittsburgh