Page template : single.php
Search terms : 
Sort : 
Page : 0

Six ways quality of life data is improving the lives of older adults

Dr. Rick Sawatzky

When we first connected with Dr. Richard (Rick) Sawatzky to chat about his work with older adults, he opened the conversation by addressing what needs to change: “Person-centred care for adults with dementia has a ways to go. More can be done to give patients a voice and to focus care on what matters most”

Having devoted his life to measuring and assessing quality of life, Dr. Sawatzky speaks from experience. He contends that the collection and use of quality of life data can have wide-reaching effects on patients, caregivers, and the health care system as a whole.

In recognition of World Alzheimer’s Month this September, here are six ways that quality of life data can improve the lives of older adults living with dementia and Alzheimer’s:

Building care around patients

Quality of life (QoL) assessments invite patients to respond to three fundamental questions: How are you? How is your care? What matters most to you? By collecting this information, health care practitioners get immediate feedback on the success or shortcomings of their interventions. Rather than focusing only on clinical data such as lab results, brain scans, or cognitive tests, QoL data focuses on all aspects of wellbeing.

“We need to look at care from the point of view of patients themselves so that we can adapt our systems to address the priorities and concerns that matter most to them,” said Dr. Sawatzky.

He has received very positive feedback from patients with dementia using QoL assessments. “Patients understand the importance of having care built around their needs.”

Dr. Sawatzky has also demonstrated that QoL measures are valid and reliable for people who have higher levels of cognitive disfunction. “This is important because traditionally people with dementia have been excluded from QoL data,” he explains.

Giving patients control over their data

QoL assessments investigate intimate aspects of patients’ lives, making it especially critical that they have control over where that information is shared. “When we interviewed patients about their thoughts on QoL assessments, the importance of personal safety came up time and time again,” Dr. Sawatzy noted.

To address this issue, Dr. Sawatzky has teamed up with Cambian health care services to develop QoL measures built around data safety. In a recent trial of QoL measurement for older adults living at home with chronic illness, the team developed an online system to administer, analyze, and store QoL data. Unlike other health data systems, in this system the patients have full control over their responses and can even choose which questions they want to be asked. This information then becomes available in an online health record, which patients can choose to share with health care providers or not.

Administering QoL surveys at home has additional benefits, including improving privacy and maintaining the quality of care for patients during the COVID-19 pandemic, an area that the study team is now specifically investigating.

Testing novel interventions

Dr. Sawatzky recently teamed up with fellow CHÉOS Scientist Dr. Amy Salmon in her evaluation of Megamorphosis. Megamorphosis aims to transform the culture of care for older adults in long-term care homes, particularly those living with dementia, from a medicalized model to a model built on relationships, staff empowerment, and patient-directed care.

“Megamorphosis is a prime example of a culture shift in health care,” said Dr. Sawatzky “But you can’t make changes to the culture of care without knowing how patients are receiving these changes.”

QoL surveys for dementia patients aim to track a wide array of metrics, including physical functioning, relationships, environment, cognition, experiences in healthcare and whether they feel like a ‘burden.’

To evaluate the success of Megamorphosis, Dr. Sawatzky supported Dr. Salmon and her research assistants Muyi Iyamu and Saranee Fernando to administer QoL surveys for older adults. Dr. Salmon’s team used this data to measure the baseline quality of care before Megamorphosis, then to assess the success of the initiative a few months later. By using QoL surveys, her team was able to measure the impact on those who matter most: the patients.

Including caregivers

Many older adults with cognitive impairment live at home and are cared for by a family member, friend, or other informal caregiver. One important aspect of Dr. Sawatzky’s approach is that he takes into consideration the wellbeing of these caregivers, thereby recognizing and celebrating the critical role they play in the wellbeing of a patient.

In QoL schemes developed by Dr. Sawatzky, primary caregivers also complete QoL surveysnot on behalf of the patient, but on behalf of themselves as both providers and recipients of care. Through this approach, health care professionals can respond to the needs of caregivers and get a sense of the broader environment that may impact patients. It can also give them a better idea of how to support caregivers in their vital work and, ultimately, improve a patient’s overall care.

Diversifying methods

As the lead of the patient-centred measurement methods cluster of the BC Support Unit, Dr. Sawatzky has teamed up with Lena Cuthbertson of the BC Office of Patient Centred Measurement to support 10 projects aimed at broadening the scope of QoL measures. These projects range from translating QoL measures into multiple languages, to tailoring surveys to vulnerably housed populations and incorporating Indigenous methodologies into QoL measurements.

Dr. Sawatzky has also recently incorporated machine learning into his QoL survey techniques. Traditional QoL measures often do not fully account for the diversity of patients’ perspectives and experiences. Dr. Sawatzky’s machine learning research focuses on the development of QoL measures that can be tailored to specific populations, taking account of things like language, culture, pre-existing medical conditions, and age. Building on an ever-growing data set, machine learning-assisted QoL surveys tailor questions to individual patients based on demographic data and their answers to previous questions. As the survey progresses, questions become more and more specific to their individual reality. This method pushes back against the ‘one size fits all approach,’ highlighting instead the individual needs of each patient.

Improving system-wide care

While it’s evident that QoL measurements can improve care at an individual level, taken collectively, QoL data also has the potential to improve health systems.

In aggregate, QoL data can help monitor impact, improve quality of care, and inform policy decisions. Unlike medical or administrative data, which measure programs from an institutional point of view, QoL data measures the success of initiatives from the ground-up.

Dr. Sawatzky admits that we’re a long way from incorporating QoL data into wide-scale decision making, but, having received overwhelmingly positive feedback from patients and health care providers alike, he remains hopeful: “The value of QoL data isn’t questioned,” he says. “Right now, it’s more about the ‘how’ how to enable people to use this data for system-wide improvements”

From early in his career, Dr. Sawatzky recognized the importance of caring for patients based on their own ideals of health and wellbeing. Gradually, he’s witnessed the idea of measuring QoL becoming more mainstream.

His work has given these types of measurements a strong methodological foundation by demonstrating that they have real value in the care of older adults, including those living with Alzheimer’s. “It’s going to take a shift in thinking to embed these things into day-to-day care,” he says. “But once that happens, our research shows that we will see improvements at both the individual and population level.”

Health research in the heart of Vancouver