Nova Scotia Department of Health allocates long-term-care beds with evidence-based decision making

The Nova Scotia Department of Health is successfully using SAS^® software to remove the guesswork from its planning and analysis activities. Its biggest success to date has been planning for new long-term-care beds at nursing homes - an area where a growing senior population and increasing demand make it critical that beds are available where they are most needed.

With over 800 new long-term care beds and a $74 million budget, a lot is at stake. Enter Evidence-Informed Decision Making. This concept provides a solid framework for allocating resources and provides a roadmap to inform further consultation and planning activities.

"With the right software tools to analyze and present the information, you can’t argue with the evidence," says Kevin Druhan, a researcher and statistician for Nova Scotia Department of Health, Continuing Care Branch. "Using SAS, we can make more informed decisions about where the beds should go and provide concrete reasons as to why."

Recently, the Continuing Care Branch undertook a major planning project around building new long-term care facilities to provide quality care for Nova Scotia’s aging population. After four months of planning, analysis, and consultation, recommendations were made around the number of beds that should be built and where they should be located. All of this work was done under the premise that needs would be best determined by understanding the health statistics of the population.

According to Druhan, SAS was a key part of the development and validation of a detailed population- and frailty-based, bed-allocation methodology that integrated data from many different sources, including Statistics Canada and the Canadian Institute of Health Information.

"We needed to analyze a lot of data quickly, and SAS was used to calculate many key components of the model, including wait times, demand drivers, population projections and the pattern of frailty in seniors across the province," says Druhan. "You need good quality information to do that, right down into the individual community."

For example, by adding population projection data supplied by Statistics Canada, "We are able to project where the highest needs will be in each area of the province," he adds. "With SAS, it doesn’t matter where the data comes from. SAS has all the tools you need to extract, clean, filter and, finally, report on the information."

Druhan also likes its flexibility in handling add-ons (some of which are custom-built) like the complex algorithm macro he uses to calculate wait times that, he says, would have been difficult to implement using other software.

It is another example of evidence-based decision making at work, says Druhan, and the results of the methodology were so comprehensive and convincing that they were accepted with minor changes by politicians and healthcare officials.

"We had strong buy-in from all stakeholders: CEOs of health districts, politicians and ultimately the public," he says. “It’s was a win-win situation for everybody."

Count Druhan among the winners too. His contribution to the long-term care bed planning work was recently recognized by the Canadian Institute of Health Information with the Innovation Award for Excellence and Creativity in Supporting Quality Care Across the Continuum. "We couldn’t have done it without SAS," he says.

Druhan first started using SAS on the OpenVMS platform to perform data quality analysis on large population-health data sets (hundreds of millions of records).

"These days, I use SAS®9 and SAS Enterprise Guide on the Windows platform to provide decision support to government. It’s become an indispensable tool for policy development, planning and performance management activities in the department," he says.

And, he’s always on the lookout for new and creative ways to use the tool. Being part of a larger community of SAS users is helpful in that regard. Beyond  training and support, he is active in the local SAS users group, in which he can exchange ideas with peers and help evolve the use of SAS within the department.

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