Marrying Healthcare Data Analytics & Privacy
Is there a more exciting, yet challenging industry than healthcare right now? After decades of promise, the industry is on the cusp of substantially improving quality of care through the use of data. Healthcare data is becoming better suited for analytics due to improving availability and reliability. Meanwhile, the industry must also figure out how to protect the privacy and confidentiality of the individuals that data represents. Further, this protection must span from the point of collection through consumption of that data by an ever increasing number of legitimate users.
Other industries have faced similar situations and have lessons to share with the healthcare industry, but there are clearly unique aspects to healthcare which the industry must figure out how to solve. Most obvious is the connection to quality of care. For every argument for how quality of care can be improved, there is an argument for why data can't be reliably collected without impacting the timeliness of care or privacy concerns.
The differentiator for healthcare is that unlike any other industry, the positive and negative sides of the argument matter more on a personal level as they deal directly with life, death and/or quality of life. While the FDIC will insure the money you have in the bank that may be lost due to bad data practices, once recovered, the loss has minimal impact to your quality of life. In healthcare, negative impacts to quality of care or reputation can't be repaired as easily as a check from the insurance company.
These consequences must be weighed heavily when it comes to data collection and analysis in healthcare, but the positive impacts to quality of care are also greater at a personal level than in any other industry. With sufficient population health and program participant data, governments can more effectively allocate funding to those health and social programs which are most effective. A complete medical record can bring significant improvements and new approaches to individual care. With more granular and complete data, medical researchers can provide increased confidence in the accuracy of their findings. The opportunity to significantly improve healthcare through data is clear.
While most think of improvements in healthcare data as beneficial to researchers in a lab or doctors in a hospital, significant improvements to healthcare can span far beyond these areas. Here are three example use cases:
- It is easy to image the improvements to quality of care possible when the highly educated and trained ER doctor has access to a complete electronic medical record of a patient not able to communicate. A less obvious, but potentially more impactful use case could be with volunteer first responders with relatively limited medical training. More complete and reliable data could significantly change both the procedures and software tools available to first responders trying to keep that same unresponsive patient alive until they can transport them to that highly educated and trained ER doctor.
- Healthcare insurance has been in crisis mode for years, partly due to the complexity of healthcare cases compared to other commonly insured items such as automobiles, property or even life. As data becomes more complete and reliable, the links between behavioral and non-behavioral risks factors and the cost of care may become quantifiable. In turn, this could allow more effective programs and financial incentives for preventative care from insurers, providers or even employers.
- One final example. In most government entities, budget cuts and allocations get spread across the board due to both politics and a lack of reliable information about the relative effectiveness of various programs. If reliable and complete data about population health and program effectiveness exist, how much more effectively can tax dollars be spent?
Here are three macro concepts to guide your organization's efforts to marry data analytics with privacy.
- Set a strategic vision for both the use and protection of data. The purpose here is to get everyone from IT to nurses, from compliance to physicians, from finance to researchers on the same page about the importance of both sides of the equation.
- Revise program and project selection processes to include alignment with the strategic vision and include formal checkpoints for data use and protection (similar to any financial gates/checkpoints). If you are going to make investments in these areas, ensure they moving you in the direction of your strategic vision.
- Share successes and lessons learned. Communicating a strategic vision is an ongoing effort that must evolve as the organization's execution of that vision evolves.
These are indeed exciting times!