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Technology and health care for the elderly medical research studies

Focus on strategic priorities that have significant social impact.

CAT 2015-16

Pilot study of an automated one-year mortality prediction tool to trigger Advance Care Planning

In this project, an automated mortality prediction tool based on the HOMR score will be developed. The feasibility of calculating the modified HOMR prospectively at the time of admission and the impact of using the tool to identify patients at risk of death, from the perspective of all stakeholders, will then be studied.

Possible Research Results

Anticipated findingsWe anticipate that it will be feasible to develop and implement this tool at the study sites. We also anticipate that the tool will help the routine identification of patients at risk of death, encouraging Advance Care Planning (ACP) conversations without being a nuisance or overly burdensome in terms of time and workload.

Impact of findingsIf we can successfully implement this tool in our pilot sites, it could be implemented at any hospital in North America and used to “trigger” any intervention appropriate for frail seriously ill patients nearing the end of their life (i.e. ACP, PC consultation, Medication Deprescription, etc.), with the potential to dramatically improve their care. Hence from a system level, this tool could also be used as part of an audit-feedback process of quality improvement - we can track the patients identified by this tool to ensure that they actually receive appropriate End-of-Life (EOL) interventions. The tool could help to identify local barriers to optimal EOL care, or provide feedback to practitioners if a large proportion of their patients are failing to receive EOL interventions. There are many ACP and EOL interventions recommended or proven to improve quality of life for frail older adults and their caregivers. However, these interventions are often overlooked or forgotten by medical practitioners because they fail to recognize that the patient is nearing the end of their life. This tool will help physicians identify the dying trajectory more routinely and accurately, which should improve the use of these interventions.

About the Project

Canadians prefer to avoid aggressive life-sustaining treatments at the end of life, but they often receive this care because their healthcare team failed to engage in ACP before they became seriously ill. It can be challenging to identify patients who are dying; even when accurate prognostic tools are available, clinicians often forget or are unwilling to use them and act on the result. If we had an accurate, automated, computer-based tool to identify patients with a limited prognosis, we could use this tool to trigger ACP and EOL interventions more appropriately and reliably.

For more details on the project rationale, hypothesis, objectives and research plan, click here.

Project Team

Principal Investigator:

James Downar, MDCM, MHSc, FRCPC -- University Health Network

Co-Investigators:

Shahin Ansari, MD -- University Health Network

Kyle Anstey, PhD -- University Health Network

Chaim Bell, MD -- Mount Sinai Hospital

Judy Costello, MD, MEd -- University Health Network

Lisa Fischer, MD, MHA -- The Ottawa Hospital

David Frost, RN, MScN -- University Health Network

Michael Hartwick, MD, MSc -- The Ottawa Hospital

Daniel Kobewka, MD, PhD -- The Ottawa Hospital

Kwadwo Kyerementang, MD, MSc -- The Ottawa Hospital

Daniel McIsaac, MD -- The Ottawa Hospital

Erin O'Connor, MD, MA -- University Health Network

Leah Steinberg -- Temmy Latner Centre for Palliative Care - Mount Sinai Hospital

Robert Wu, MD, MPH -- University Health Network

John You, MD -- McMaster University

Knowledge Users and Partners:

Russell Goldman, MD -- Temmy Latner Centre for Palliative Care - Mount Sinai Hospital

Carl van Walraven, MD, MSc -- Institute of Clinical and Evaluative Sciences, Ottawa Hospital Research Institute

Project Contact: James Downar -- james.downar@utoronto.ca

CAT 2015-16

Key words: prognosis; medical informatics applications; medical record systems; computerized; advance care planning; qualitative research