Data science is the field of techniques, tools and frameworks used to study and make meaningful conclusions from data. Data is being collected at an accelerated rate and the techniques, tools, and frameworks available to data scientists have evolved significantly over the last several years. The growth of this field has created tremendous opportunity as well as challenges for leaders in the healthcare industry. As this field evolves, healthcare leaders will need to become more knowledgeable about what investments are required in order to make the best use of their data.
There are ten key points for healthcare executives to consider when forming a data science strategy:
Data is an asset to organizations in a similar way that buildings and medical devices are assets. An increasing share of the present and future value of health systems will be derived from the type of data that is collected and how effectively it is used to meet clinical, financial, operational, and strategic goals.
As the pressure to cut costs and demonstrate high quality care continues to mount, understanding the types of data assets available and also the ones necessary to acquire is increasingly important. Consider how data assets are positioning the organization for future success…or future difficulty. For example, if a data element needed for calculating a quality metric that will be publicly reported in two years is not yet being captured, determine how quickly it can be captured. Two years from now, when that data becomes publicly reported, there will be no ability to go back and create a historical data record to use, and the organization could be understating the quality of care they provide simply because they don’t have the historical data to prove how well they actually performed.
In the coming years, some organizations will thrive and others will struggle. Organizations that make smart use of data and analytics will have a strong competitive advantage over those that do not. Increasing pressure to cut costs and improve quality means increasing pressure to understand as quickly as possible the factors or organizational behavior that are contributing to positive as well as negative results.
When planning to acquire the appropriate data assets, anticipate how the organization will make the best use of these assets once they have been acquired. Data science helps organizations start to understand some of the important relationships between practice patterns or other organizational behavior and undesired or desired outcomes. For example, it is important for organizations to know that their average length of stay at a particular hospital is increasing, but it is at least equally important to know why this increase is occurring and what, if anything, can be done about it. Data science helps uncover the why and enables organizations to make more informed strategic decisions.
One general principle for executives to keep in mind when thinking about data science is that good decisions require good knowledge, that good knowledge requires asking good questions of data, and that it is impossible to know what the “good questions” are unless you understand the scope of questions that are answerable. In other words, 30 years ago it would have been fruitless for executives to be asking questions like “what variables contribute to readmission risk and to what extent” because methods for answering that question did not exist in the way that they do today.
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