Data-driven Strategy as an Organizational Approach for Effective Healthcare Data Analytics Implementation
Data-driven strategy can assist healthcare professionals in their efforts to improve three key areas: quality, availability, and satisfaction. HCOs should always strive towards improvements in these three areas since together they create the foundation of quality, accessible, effective care. What’s more, when these three areas are addressed effectively, overall trust in the healthcare system increases which makes it a more positive and productive environment for caregivers and patients alike. Data-driven strategy can also inform the proper implementation of healthcare data analytics, which together with the accomplishment of the aforementioned HCO goals strengthens the system even further.
Data-driven Strategy and HCO Goals
There are three key goals that every healthcare organization seeks to achieve. First, HCOs seek to improve the overall quality of care offered to their patients. Secondly, they seek to expand the availability of and access to healthcare for demographics and populations that have been historically or are currently underserved. Finally, they aim to increase overall satisfaction with the healthcare system and the services it provides so as to garner patient trust. Data-driven strategy can help HCOs accomplish all three of these objectives.
Of course, one of the main goals of the healthcare system and HCOs is to improve the quality of care. Offering the best quality of care possible means that patients get better faster and have an overall trust in the healthcare system. Improved quality doesn’t end at patient care, though. Quality exists in a number of iterations throughout the healthcare system. Healthcare technology users expect high levels of quality in the products and services they use. Those who utilize medical devices or take prescriptions expect them to be structurally sound and/or effective. When HCOs design strategy and policy, the main goal is usually to create an efficient system that better serves the patient, which, in effect, is quality.
The second major goal of HCOs and the healthcare system at large is to find ways to improve and increase the availability of care. For some populations and demographics, access to healthcare has been notoriously slim. If they do manage to obtain care, the quality of that care tends to be lower. For those who have been adversely affected due to geography, demography, or other factors, not having quality healthcare available can be the difference between life and death. That’s why HCOs and healthcare professionals work so hard to try to facilitate access to quality healthcare for everyone. While equity is a long way off, Big Data can help inform those who are actively working to create an equitable system.
Finally, patient satisfaction is an important metric of the success of an HCO or individual healthcare provider. If patients aren’t satisfied with the care they receive and don’t have trust in the system, it can become difficult to engage them in their own care. However, when quality and access increase and patients see the benefits thereof, their overall impression of the healthcare system and satisfaction with it increases. This helps to facilitate a situation in which patients are more likely to seek out care, and, when they do, are more likely to follow physician instructions and be active participants in their own health and well-being.
Healthcare Data Analytics and Data-driven Strategy
Utilizing data-driven strategy can lead to a better understanding of system-wide healthcare needs. This heightened level of understanding and the increased accuracy that comes with it can pave the way toward more efficient and effective analytics. The implementation of properly performed healthcare data analytics opens doors to a host of benefits for HCOs and patients alike.
For one thing, personalized care becomes far more attainable when data-driven strategy informs healthcare data implementation objectives. When reliable data from various sources work together to create actionable information about the specific needs of individual patients as well as demographic, geographic, and epidemiological needs, personalized care becomes a reality. This personalization tends to increase feelings of trustworthiness and satisfaction in patients and also leads to more effective and higher quality care.
Predictive Analytics and Diagnosis
Predictive analytics can further progress in the area of diagnosis by helping healthcare practitioners stay ahead of the game. When trends emerge, predictive analytics can provide important and timely healthcare data that tell physicians what to expect. If data trends indicate a strong potential of forthcoming illness, whether it be in a single patient or in a population, that can drastically improve the efficiency with which the physician can create treatment plans and treat, if not prevent, the illness. How this can improve the overall quality of healthcare does not need to be elaborated upon.
Increased Quality, Decreased Costs
When Big Data informs healthcare analytics, it becomes much easier to improve the financial efficiency of the system. By utilizing healthcare data and implementing data analytics properly, HCOs and administrators can see where waste is occurring, where funds are lacking, and how best to balance that financial situation. When HCOs become more fiscally efficient, the cost of care decreases for everyone involved and the quality of care increases.
When HCOs use data-driven strategy as an intentional, organizational approach to improving healthcare through more effective healthcare data analytics implementation, the entire system benefits. Using a data-driven strategy to help accomplish the three main goals of any HCO means more accurate information is obtained, more effective treatment can be designed, and a more efficient system exists because of it. Taking that strategy and using it to implement healthcare data analytics in a purposeful way can also increase quality, improve patient satisfaction, help physicians make better decisions and more efficient diagnoses, and can ensure the appropriate and effective utilization of available data. While transitioning to a new approach can be a challenge, the benefits are worth it, and when making that transition becomes inevitable, early adopters will be ahead of the curve.