Clinical trial NCT04171830: Clinical Surveillance Tool to Screen for Unmet Palliative Needs Among Patients in the Final Year of Life
Official title: Use of an Automated Prospective Clinical Surveillance Tool to Drive Screening for Unmet Palliative Needs Among Patients in the Final Year of Life
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Palliative Therapy
Implementation Science
Screening
Terminal Care
Keywords: palliative care
end of life
clinical surveillance
Lead sponsor: Ottawa Hospital Research Institute
Sponsor class: OTHER
Collaborators: name: Canadian Frailty Network
name: Centre for Aging and Brain Health Innovation
name: Canadian Foundation for Healthcare Improvement
name: Bruyere Research Institute
name: William Osler Health System
name: Hopital Montfort
name: Queen's University
name: Pembroke Regional Hospital
name: Unity Health Toronto
name: Cambridge Memorial Hospital
name: The Ottawa Hospital
name: Queensway Carleton Hospital
name: University of Ottawa
name: The Hospital for Sick Children
name: ICES
name: London Health Sciences Centre
name: Windsor Regional Hospital
name: Humber River Hospital
name: North York General Hospital
name: Ontario Health - Quality
name: Healthcare Excellence Canada
name: Headwaters Health Care Centre
First posted: 2019-11-21
Enrollment: 3536
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: This study is a non-randomized hybrid implementation-intervention comparative trial using pre- and post-implementation methods with an embedded qualitative component pre- and during intervention.
Primary purpose: SCREENING

Brief summary: 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.

Detailed description: 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

To determine whether implementation of an mHOMR or HOMR Now! application to identify patients at increased risk of death and trigger screening for unmet palliative needs improves (1) identification and documentation of those needs and (2) the end-of-life care provided to patients.

Intervention-Implementation Procedures

Every inpatient at each site will automatically be given the intervention (an mHOMR or HOMR Now! Score, depending on which application can be most easily integrated into each site's existing EMR system) upon admission to an implementing unit at a participating hospital and considered for secondary interventions (i.e. palliative care) based on their score.

At a minimum, each individual identified by the chosen HOMR tool should receive two additional assessments to screen for severe symptoms and the patient's desire to engage in advanced care planning (ACP):

1. Edmonton Symptom Assessment System Revised (ESAS-R): scores of \>6 will be flagged as 'severe'. Individual clinical teams can then choose to address the symptoms as appropriate for the patient, or consult a PC team.
2. 4-item Advanced Care Planning Engagement Survey: Scores of 3-4 indicate a patient is ready to discuss ACP with a member of the clinical team. Clinical teams may choose to discuss ACP and goals of care (GoC) themselves, activate a local ACP/GoC intervention, or distribute ACP documentation (e.g. SpeakUp resources), as applicable.

Implementation of the mHOMR/HOMR-Now! intervention in each site will follow 4 phases informed by the Quality Implementation Framework:

1. Site-Specific Considerations

   To facilitate successful implementation of the mHOMR/HOMR-Now! application, three strategies will be used to tailor implementation to the site-specific context, including needs, resources, fit, capacity, and readiness. Firstly, members of the coordinating and implementation research team will virtually conduct a detailed readiness assessment to determine the best way to implement the application given the local context of each site. Secondly, semi-structured focus group interviews will be held virtually at each site with the Implementation team (i.e. an executive champion, implementation lead, clinical lead, and information technology lead), as well as staff who will interact with the HOMR application output (i.e. either recipient of or actors on the notification). Lastly, select members of the implementation team will be virtually interviewed individually to determine site-specific implementation barriers and facilitators.

   Considering the unique site context, investigators will then work with stakeholders to determine the additional interventions that each site will implement once a patient has been identified as being at elevated risk for mortality and unmet palliative needs by the application. Notably, determination of secondary interventions will consider the normal workflow and resources available at each site. Details of these interventions will be provided in each site's individual protocol.
2. Establishment of Information Technology Infrastructure for Implementation at each Site

   Logistics of the mHOMR or HOMR-Now! application will be discussed with the IT lead at each site to determine the technical approach for implementation in the electronic health record (EHR), including which application would be most appropriate to implement given each site's existing EHR. Some EHR platforms are used by more than one site; thus, solutions derived for one EHR will be shared among other partners as appropriate.

   Once the specific technical implementation process has been defined for each site, electronic and print educational material will be developed to teach staff at each site about mHOMR/HOMR-Now!, how the application works, and steps to take when they receive a notification.
3. Development, Deployment, and Ongoing Support for mHOMR/HOMR-Now! Implementation

   Integration of the application into each site's EHR will be managed by the site IT lead and tested to ensure the application is correctly calculating the mortality risk scores and notifying the appropriate members of the care team. Once this is complete, each site will host a "go-live" kick-off event to help generate awareness, enthusiasm, and uptake of the intervention.

   In the following six to nine months (depending on the site's funding source), the application will be 'live' at each site, actively identifying newly admitted patients and notifying care teams to conduct the ESAS-R and 4-item ACP Engagement Survey as appropriate. Any additional site-specific interventions will also be implemented.

   Process and outcomes evaluations will also occur during phase 3. In addition to phase 1 interviews and focus groups, these evaluations will involve: (1) a second set of semi-structured interviews with members of the implementation team at each site to examine determinant factors associated with successful implementation of the mHOMR application; (2) a chart review to examine clinical and implementation outcomes, and; (3) analysis of linked health administrative data held at ICES to evaluate long-term clinical outcomes.
4. Continuous Improvement of mHOMR/HOMR-Now! Implementation (Concurrent with Phases 1-3)

Each site will be regularly updated of study progress through teleconferences and newsletters. These communications will also share learnings across sites to improve implementation through establishment of best practices and identification of strategies to overcome implementation barriers. Additionally, this process will inform implementation of the application in other hospitals in the future.

All clinical secondary outcomes will be measured for HOMR positive patients for six to nine months pre and post implementation, and followed until end of study follow-up (up to one year after hospital discharge) or death. While each of these outcomes will be measured, aggregated, and linked to databases held at ICES, results from individual site data will be used to improve patient care by driving existing clinical best practices for palliative care, symptom management, and advanced care planning. In this sense, the clinical secondary outcome measures will also serve as indicators for continuous quality improvement at each hospital.

Eligibility criteria: Inclusion Criteria:

* 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).

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Identification and documentation of unmet palliative needs
timeFrame: Through study completion, up to 9 months
measure: Identification and documentation of unmet palliative needs
timeFrame: Through study completion, up to 9 months
Secondary outcomes:
measure: Acceptability - Qualitative perception of the implementation team that the application is agreeable, palatable, or satisfactory: The Hexagon Tool
timeFrame: 1-3 months pre-intervention-implementation
measure: Adoption: the intention, initial decision, or action to employ the application
timeFrame: Through study completion, up to 9 months
measure: Appropriateness - implementation team and staff perceived fit, relevance, or compatibility of the application for a given setting
timeFrame: 1-3 months pre-intervention implementation and 1 month post study completion (at 10 months)
measure: Cost of delivering the mHOMR/HOMR-Now! application
timeFrame: Through study completion, up to 9 months
measure: Feasibility - extent to which the application can be successfully used within a given hospital's context
timeFrame: 1 month post study completion (at 10 months)
measure: Fidelity to application implementation protocol
timeFrame: Through study completion, up to 9 months
measure: Fidelity to application implementation protocol
timeFrame: Through study completion, up to 9 months
measure: Penetration
timeFrame: Through study completion, up to 9 months
measure: Effectiveness
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Effectiveness
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Effectiveness
timeFrame: Up to 52 weeks post hospital discharge or death, whichever is first
measure: Effectiveness
timeFrame: Up to 52 weeks post hospital discharge or death, whichever is first
measure: Effectiveness
timeFrame: Up to 52 weeks post hospital discharge or death, whichever is first
measure: Effectiveness
timeFrame: Up to 52 weeks post hospital discharge or death, whichever is first
measure: Effectiveness
timeFrame: Up to 52 weeks post hospital discharge or death, whichever is first
measure: Effectiveness
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Effectiveness
timeFrame: At time of death, up to 52 weeks post-discharge
measure: Patient-centredness
timeFrame: At time of death, up to 52 weeks post-discharge
measure: Safety - Conditions/Harms
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Safety - Infections
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Safety - Patient Accidents
timeFrame: Measured at inpatient hospital discharge for entire inpatient stay duration (average of 2 weeks)
measure: Efficiency
timeFrame: Up to 52 weeks post-discharge or death, whichever is first
measure: Efficiency
timeFrame: Up to 52 weeks post-discharge or death, whichever is first
measure: Care Coordination
timeFrame: At hospital discharge
measure: Care Coordination
timeFrame: Up to 1 week post-discharge
measure: Care Coordination
timeFrame: Up to 1 week post discharge
measure: Care Coordination
timeFrame: At hospital discharge
measure: Care Coordination
timeFrame: Up to 52 weeks post-discharge or death. whichever is first
Other outcomes:
Locations:
city: Brampton
country: Canada
facility: William Osler Health System
state: Ontario
city: Cambridge
country: Canada
facility: Cambridge Memorial Hospital
state: Ontario
city: Kingston
country: Canada
facility: Kingston Health Sciences Centre
state: Ontario
city: London
country: Canada
facility: London Health Sciences Centre
state: Ontario
city: Orangeville
country: Canada
facility: Headwaters Health Care Centre
state: Ontario
city: Ottawa
country: Canada
facility: The Ottawa Hospital
state: Ontario
city: Ottawa
country: Canada
facility: Montfort Hospital
state: Ontario
city: Ottawa
country: Canada
facility: Queensway Carleton Hospital
state: Ontario
city: Pembroke
country: Canada
facility: Pembroke Regional Hospital
state: Ontario
city: Toronto
country: Canada
facility: North York General Hospital
state: Ontario
city: Toronto
country: Canada
facility: Humber River Hospital
state: Ontario
city: Toronto
country: Canada
facility: Saint Michael's Hospital
state: Ontario
city: Windsor
country: Canada
facility: Windsor Regional Hospital
state: Ontario
Study officials:
References:
citation: van Walraven C. The Hospital-patient One-year Mortality Risk score accurately predicted long-term death risk in hospitalized patients. J Clin Epidemiol. 2014 Sep;67(9):1025-34. doi: 10.1016/j.jclinepi.2014.05.003. Epub 2014 Jun 25.
citation: van Walraven C, McAlister FA, Bakal JA, Hawken S, Donze J. External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission. CMAJ. 2015 Jul 14;187(10):725-733. doi: 10.1503/cmaj.150209. Epub 2015 Jun 8.
citation: van Walraven C, Forster AJ. The HOMR-Now! Model Accurately Predicts 1-Year Death Risk for Hospitalized Patients on Admission. Am J Med. 2017 Aug;130(8):991.e9-991.e16. doi: 10.1016/j.amjmed.2017.03.008. Epub 2017 Mar 31.
citation: Wegier P, Koo E, Ansari S, et al. mHOMR: A pilot study of automated prospective clinical surveillance for inpatients having an elevated risk of one-year mortality. Under Review.
citation: Watanabe SM, Nekolaichuk C, Beaumont C, Johnson L, Myers J, Strasser F. A multicenter study comparing two numerical versions of the Edmonton Symptom Assessment System in palliative care patients. J Pain Symptom Manage. 2011 Feb;41(2):456-68. doi: 10.1016/j.jpainsymman.2010.04.020. Epub 2010 Sep 15.
citation: Sudore RL, Heyland DK, Barnes DE, Howard M, Fassbender K, Robinson CA, Boscardin J, You JJ. Measuring Advance Care Planning: Optimizing the Advance Care Planning Engagement Survey. J Pain Symptom Manage. 2017 Apr;53(4):669-681.e8. doi: 10.1016/j.jpainsymman.2016.10.367. Epub 2016 Dec 29.
citation: Meyers DC, Durlak JA, Wandersman A. The quality implementation framework: a synthesis of critical steps in the implementation process. Am J Community Psychol. 2012 Dec;50(3-4):462-80. doi: 10.1007/s10464-012-9522-x.
citation: Metz A, Louison L. The Hexagon Tool: Exploring Content. Chapel Hill, NC: National Implementation Research Network, Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill; 2018:1-5.