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Overcoming cost and manpower limitations with self-service data analytics

February 21, 2024
Business Affairs Health IT
Peter Calderone
By Peter Calderone and Deanna Rothwell

For many health systems and hospitals, the process and methodology of using real-world data to inform care and operational quality improvements is a long, onerous slog.

Specifically, gaining access to data in a timely manner while having the capacity and resources for complex analysis represents a significant challenge for most hospitals.

Health system leaders, of course, understand the importance of leveraging data to discover new ways of driving higher-quality patient care and better financial and operational performance, but they typically run into two major issues when attempting to implement these projects: cost and manpower.

U.S. healthcare providers have long faced tight budgets that leave limited funds for data analytics initiatives – and such challenges are not unique to this country. In Canada, for example, the healthcare data universe and use cases match the US experience with the added constraint of even lower IT and workforce budgets. To overcome their significant financial and manpower barriers, several healthcare organizations across Canada, the U.S., and Israel have deployed nimble organizational structures and self-service analytics to drive innovative performance-improvement initiatives.

The old way: Expensive data analysts and restricted data access for clinicians
Traditionally hospitals and health systems have derived insights from their own data by employing large teams of data analysts. For the clinicians and health system leaders developing hypotheses and questions, they send their queries to the data analytics team, who then must comb the health system’s data to assemble the right data set. For the data analysts, they spend their days re-pulling and refining data sets in administrative cycles that can take weeks and months. This can be a lengthy, convoluted process that forces curious clinicians and leaders to wait in line while high-value data analysts are wrangling menial data requests. Both groups involved in this equation are restricted from focusing on the high value, strategic initiatives and the health system suffers when this limited flow of research projects results in very little return on investment over time.

For clinicians, researchers, and administrators who are looking for answers to enhance care quality, the process is discouraging. For health organizations, the process is inefficient and expensive. In contrast, a self-service approach allows health systems to eliminate these bottlenecks.

Kim Sr Luy

Self-service Data Analytics approach

February 22, 2024 04:13

Agree 💯% with Self-service Data Analytics approach.

Kindly expound on its process flows.
What are applicable Softwares/System to be used?

Thank You

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