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The Rise of Data and Decision-Making

By Jef Williams

You are likely working on an analytics or business intelligence (BI) initiative. It seems like we all are. But how do we think about this data we have been told is so important to capture, aggregate, synthesize, curate and display? When you consider the early days of net searches, it wasn’t easy using those early engines that brought limited results and often required secondary searches that were inefficient. The rise of Google brought a wave of information to our fingertips in ways that have changed how we access that information. However, the real value of Google was not in how much information it could retrieve, but in how it applies rules of relevance to the searcher. Just having access to all of the data is almost useless unless it comes to us in a way that is indexed by appropriateness, relevance and prioritization.

Historically, much of the data we leverage within health care was limited. In recent years software solutions and platforms have emerged that are designed to retrieve and curate data points across devices, operations, technology, workflow, utilization and experience. But just “turning on” the faucet creates a waterfall of information that can create more problems that solutions. Useful data must be modeled, organized and curated for the user who needs the information. This requires effort in design that happens well before collecting data.

As we look at analytics, we must begin with a model that is informed by the outcome of the data. Most successful projects begin with a design phase that builds from preferred outcomes backward. Knowing how you want to use data critically informs how you capture, model and visualize those data.

Analytics and BI have become ubiquitous in health care as the technologies to aggregate, synthesize and report have increased. Several years ago, there were very few ways to gather disparate data elements and bind them together (think modality utilization and staffing levels) outside of hiring someone to build spreadsheets and perform analysis. Now there are a large number of companies that can ingest data from multiple sources using a number of different formats and automate some level of analysis. This has given us ways to view our organizations and their performance like never before.

Here is where leadership must provide the impetus for change. Too often the ability to visualize trends or report on key performance indicators (KPIs) sits on desktops, in inboxes, simply viewed and deleted. This can be due to apathy, mistrust or inertia.

Apathy may be a strong word – but there are those who simply don’t care about the data. The move toward evidence-based decision-making is like any other transition in business – there are those who embrace and those who resist change. Some leaders believe their experience, their “gut” or their traditions are suitable for assessing and directing business strategy and management. It goes without saying that this type of leadership is nearly always shortsighted. We must always practice self-awareness to ensure we aren’t demonstrating, supporting or encouraging this kind of behavior in our organizations.

Mistrust is a by product of bad input, bad systems or bad methodology. Each of these can be remediated, albeit with some difficulty and specificity. When there is mistrust, there should be red flags that call attention to a fundamental problem that no amount of color coding or system promoting activity can remediate. Mistrust happens when the system’s design and build is not done carefully, when the data that is being ingesting and analyzed is not modeled correctly, or when the outputs and analysis do not accurately reflect the true nature of what is being measured. Leaders must call attention to mistrust as a critical concern in analytics. Without trust, the entire investment of time, resources and capital are wasted in an analytics initiative.

Inertia is often driven by organizational structure. Having the right data, valuing that data and identifying how those data inform strategy and execution are barely half the work. The other half is tactically delivering the necessary change to improve whichever KPI is being addressed. Governance does not get enough attention in analytics efforts. That is, providing the authority to both the decision-makers as well as the directors and managers, to implement change. Health care is a slow-moving industry and we must relearn some old habits to embrace agility and dexterity as organizations to overcome deficiencies and achieve better outcomes.

We all believe data is important and well-informed leaders make decisions based on facts. But the implementation of technology and methodology also require our involvement. We must consider not only the data nor solely the technology. We must consider our leadership and our organizations that are dependent on action in designing, implementing and interpreting the information.

Jef Williams, MBA, PMP, CIIP, is a managing partner at Paragon Consulting Partners.

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