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Financial Management in Health Care HLTH405 IP 3
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Research information about current considerations and challenges related to the financial and budgetary systems in health care organizations. Consider the use of data analytics and tools in the monitoring, assessing, and evaluating of the performance of health care organizations. Include a discussion of the importance and efficacy of financial statements used in the decision-making process of health care organizations.
Support your work with at least 4 academic or professional peer-reviewed sources published within the past 5 years.
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Lessons Learned: Bringing Big Data Analytics To Health Care
Steven Escaravage and Joachim Roski
July 14, 2014
Big data offers breakthrough possibilities for new research and discoveries, better patient care, and greater efficiency in health and health care, as detailed in the July issue of Health Affairs. As with any new tool or technique, there is a learning curve.
Over the last few years, we, along with our colleagues at Booz Allen, have worked on over 30 big data projects with federal health agencies and other departments, including the National Institutes of Health (NIH), Centers for Disease Control (CDC), Federal Drug Administration (FDA), and the Veterans Administration (VA), along with private sector health organizations such as hospitals and delivery systems and pharmaceutical manufacturers.
While many of the lessons learned from these projects may be obvious, such as the need for disciplined project management, we also have seen organizations struggle with pitfalls and roadblocks that were unexpected in taking full advantage of big data’s potential.
Based on these experiences, here are some guidelines:
Acquire The “Right” Data For The Project, Even If It Might Be Difficult To Obtain.
We’ve found that many organizations, eager to get started on a big data project, often quickly gather and use the data that is the easiest to obtain, without considering whether it really goes to the heart of the specific health care problem they’re investigating. While this can speed up a project, the analytic results are likely to have only limited value.
For example, we worked with a federal agency experimenting with big data analytics to identify cases of perceived fraud, waste, or abuse. The program’s analysts focused on data they already had on hand and currently used to direct audit and investigation activity. We encouraged project staff to identify alternative data sources that might reveal important information about compliance history or “hotspots” for illegitimate activity.
We learned that historical case reports and online provider marketing materials were available and were a potentially valuable source for information to aid in fraud detection. However, the project analysts had decided it would take too long to incorporate that information and so had excluded it.
Many organizations – both inside and outside of health care – tend to stick with the data that’s easily accessible and that they’re comfortable with, even if it provides only a partial picture and doesn’t successfully unlock the value big data analytics may offer. But we have found that when organizations develop a “weighted data wish list” and allocate their resources towards acquiring high-impact data sources as well as easy-to-acquire sources, they discover greater returns on their big data investment.
Ensure That Initial Pilots Have Wide Applicability.
Health organizations will get the most from big data when everyone sees the value and participates. Too often, though, initial analytics projects may be so self-contained that it is hard to see how any of the results might apply elsewhere in the organization.
We ran into this challenge when we helped a federal health agency experiment with big data analytics. The agency’s initial set of pilots focused on specific, computationally complex and storage-intensive challenges, such as reconfiguring a bioinformatics algorithm to run across a large cluster of processors and developing a data-capture approach to access and store data in real time from a laboratory instrument.
While each pilot solved a big data analytics challenge, the resulting capabilities did not provide examples that would be powerful enough to push transformational change across the organization, as the organizational leaders had hoped.
In subsequent pilots, we advised the agency to focus on less rigorous but more far-reaching pilots. In one project, the agency piloted an unstructured natural language processing and text search utility across a number of disparate data archives. In another project, we deployed a data platform that could rapidly generate millions of records of synthetic data for algorithm testing.
In each case, organizational decision-makers could more easily see the applicability and potential of big data analytics and more clearly understand the potential of big data to transform their organization.
Before Using New Data, Make Sure You Know Its Provenance (Where It Came From) And Its Lineage (What’s Been Done To It).
Often in the excitement of big data, decision-m
