Use of an Automated Prospective Clinical Surveillance Tool to Drive Screening for Unmet Palliative Needs Among Patients in the Final Year of Life
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 3,536
- 试验地点
- 25
- 主要终点
- Identification and documentation of unmet palliative needs
研究概览
简要总结
One of the most important obstacles to improving end-of-life care is the inability of clinicians to reliably identify those who are approaching the end-of-life. Every aspect of a palliative approach to care - screening for unmet needs, treating symptoms, discussing goals of care, and developing a palliative management plan - depends on the reliable and accurate identification of patients with palliative needs. The investigators developed an accurate and reliable mortality prediction tool that automatically identifies patients in hospital at elevated risk of death in the coming year. In previous studies it has been shown that these patients also frequently have unmet palliative care needs at the time they are identified by the tool. This tool has been demonstrated feasible, acceptable to patients and providers, and effective for changing physician behaviour in an inpatient clinical context.
In this project, this tool is implemented as part of an integrated knowledge translation project to facilitate reliable and timely identification of unmet palliative needs across multiple hospitals with different clinical settings and contexts. The investigators have partnered with 12 hospitals to improve the quality of palliative and end-of-life care provided to patients and families. With each partner site the investigators will develop a comprehensive implementation plan, including stakeholder engagement, education, and feedback. Process measures will be collected at each site to determine whether the tool was effective for promoting the identification and documentation of unmet palliative needs. Patients who were identified by the tool will also be followed over time to collect outcome and impact measures to see if their end-of-life care was affected by the intervention compared to control groups.
详细描述
Recently, van Walraven et al described the Hospital One-year Mortality Risk (HOMR) score for predicting 1-year mortality for patients admitted to hospital. HOMR is based on 12 administrative data points routinely coded by hospitals at the time of discharge and available in the CIHI Discharge Abstract Database. The model has been externally validated with excellent discrimination and calibration. Among HOMR's 12 data fields, nine are routinely available in the Electronic Health Record (EHR) at the time of admission in Ontario. Using a method similar to that used to derive HOMR, the investigators developed a "modified" HOMR (mHOMR) model based on the nine data fields available at the time of admission. mHOMR had comparable accuracy to HOMR (C-statistic .89 vs .92, respectively). Additionally, an updated version of mHOMR has recently been developed and validated, called HOMR Now!, which has the same c-statistic as the original HOMR (.92) but is calculated using ten data fields and an interaction available in many hospital admissions data, similar to mHOMR. Using either mHOMR or HOMR Now!, hospitals are able to retrieve admissions data from the EMR and calculate each patient's mortality risk on admission. If any patient's mortality risk exceeds a predefined threshold, the application would send a message to their clinical team prompting them to assess and address unmet palliative needs.
mHOMR has been implemented in four hospitals in Ontario to date and has been adapted to work with different EHRs. The mHOMR application identified a gender-balanced cohort of generally elderly patients (mean age of 83 years) who were admitted for several days (median length of stay of 5 days) and discharged alive (89%), meaning they were not in their final days of life and there would be an opportunity to screen for unmet needs and participate in care planning. A second pilot study found >90% of patients identified by the application had an unmet palliative need- either a severe symptom or a desire to discuss ACP with a physician or both-and that patients with higher mHOMR scores had more severe symptoms. The application preferentially identified patients with non-cancer illnesses-most were admitted with a frailty-related condition (56.8%), followed by end-stage organ failure (23.5%), and cancer (20%)-meaning that the tool did not show a bias towards cancer but instead identified patients who reflected the actual population of dying Canadians. These results are similar to findings from HOMR Now! validation work. Furthermore, investigators found <50% of those identified by mHOMR had a documented palliative care consultation or Goals of Care discussion, but after the integration of mHOMR notifications into existing workflow, the incidence of early Goals of Care discussions and palliative care consultation increased significantly. Additionally, qualitative results show the application is acceptable to patients and clinicians alike.
Both the mHOMR and HOMR Now! applications are intended to be a reliable and accurate "trigger" to improve the effectiveness of any palliative intervention by focusing attention on a small group of patients with a high risk of death and unmet palliative needs. Both applications can also be versatile depending on the situation-it produces a numerical risk output rather than a binary yes/no like the Surprise Question, Gold Standards Framework or NECPAL tools, so the user can decide what threshold to use for identifying "high risk" patients. Thus, organizations concerned with the efficient use of limited resources could set a higher mortality threshold, while organizations using more scalable interventions could lower the mortality threshold.
Given the initial success of the mHOMR and HOMR Now! applications in identifying unmet palliative care needs in an acceptable way among patients nearing the end-of-life, the next step is to implement and rigorously evaluate the immediate long-term effects of this highly scalable intervention in a large population to determine whether it improves screening and documentation processes and ultimately leads to better outcomes for patients, family members, and the healthcare system as a whole. To achieve this aim, investigators have partnered with twelve acute care hospitals from across Ontario to implement the mHOMR/HOMR-Now! intervention.
Objective
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All newly admitted patients to selected medical units in participating sites during the 6-9-month intervention implementation period
- •[To be assessed for unmet palliative needs] the patient must be competent and have the ability to participate in assessments (i.e. answer assessment questions and understand and speak sufficient English to participate).
排除标准
- •N/A for mHOMR/HOMR-Now! intervention
- •For palliative needs assessments: incapability of completing the ESAS and 4-Item ACP tools, either because of capacity/cognitive impairment or English-language ability.
结局指标
主要结局
Identification and documentation of unmet palliative needs
时间窗: Through study completion, up to 9 months
Proportion of admissions at each site identified by the application
次要结局
- Safety - Conditions/Harms(Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks))
- Adoption: the intention, initial decision, or action to employ the application(Through study completion, up to 9 months)
- Safety - Patient Accidents(Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks))
- Care Coordination(Up to 52 weeks post-discharge or death. whichever is first)
- Feasibility - extent to which the application can be successfully used within a given hospital's context(1 month post study completion (at 10 months))
- Penetration(Through study completion, up to 9 months)
- Effectiveness(At time of death, up to 52 weeks post-discharge)
- Acceptability - Qualitative perception of the implementation team that the application is agreeable, palatable, or satisfactory: The Hexagon Tool(1-3 months pre-intervention-implementation)
- Appropriateness - implementation team and staff perceived fit, relevance, or compatibility of the application for a given setting(1-3 months pre-intervention implementation and 1 month post study completion (at 10 months))
- Cost of delivering the mHOMR/HOMR-Now! application(Through study completion, up to 9 months)
- Patient-centredness(At time of death, up to 52 weeks post-discharge)
- Safety - Infections(Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks))
- Fidelity to application implementation protocol(Through study completion, up to 9 months)
- Efficiency(Up to 52 weeks post-discharge or death, whichever is first)
研究者
James Downar
Principal Investigator
Ottawa Hospital Research Institute