Making Data-Driven Decisions in Healthcare
6 Considerations During a System Implementation
By Stephanie Fiano and Marissa Iannarone
It is estimated we make 35,000 decisions per day: approximately 1 decision every 2 waking seconds.[1] Some decisions are muscle memory (i.e., brushing teeth), but the most important decisions require attention, slowing down, minimizing noise, consulting trusted advisors/peers, and analyzing data.
Making decisions with insufficient information is akin to guessing, creating risk. Clearly the healthcare industry agrees, with a plethora of data-related efforts to support decision making and mitigate risk, including numerous initiatives to identify and support the most vulnerable populations of COVID-19[2]; the recent deal between Google and HCA[3] to develop algorithms guiding doctors’ decisions; the deployment of Epic Software’s predictive analytics tools[4] helping physicians determine when intensive care is needed.
The healthcare industry relies heavily on reliable data to make life-changing, decisions. And yet, technical tools within an organization are often changed without considering the impact on currently available data, or how the data resulting from the new tool can add value.
One of the best ways to ensure relevant, accurate, real-time data is available for decision support is to proactively address data at the start of a system implementation. To support decision-making, consider the following when implementing any new system, regardless of industry:
Acknowledge the data impact. Regardless of presentation (dashboard, KPIs, email) or audience (leadership, operations, clinicians) someone is using the data in the current system to make decisions. Changing the system disrupts their ability to access data to make those decisions. Acknowledging the value of available data prior to the new system implementation is critical.
Partner with data end-users. To alleviate anxiety and mitigate gaps caused by changing availability of data, include users of the current data and potential users of the new data in planning and implementing the new system. Discuss how information is used and what (and how) data will be available in the future. Partnering with data-related teams (i.e., business intelligence) is crucial to understanding data flow and managing the impact to end-users.
Start with the decision point in mind. Data needs are best communicated through use cases, specifically what decisions need to be made. Once a decision point is well understood the team can identify the relevant data to support it, eliminating extraneous noise, and confirm details about data availability and format that enables clear and quick decision making.
Assess opportunities for improvement. There may be opportunities to improve the data used to make decisions. Perhaps the decision is no longer necessary because the workflow or business process has changed (and thus the data is no longer required). There may also be new data available that will enhance the decision-making process in a team. Engaging with end-users to identify opportunities for improvement demonstrates a collaborative approach to making the best use of the data available.
Promote self-service and inclusion. Self-service alleviates bottlenecks, promotes transparency and inclusion, and mitigates individual biases when multiple people interpret the same data. Self-service may be a cultural shift, requiring collective agreement about privacy, security, and ethics related to data access.
Address the data lag and develop steady state expectations. As implementations progress and end-users become familiar with the new data, set expectations early about data availability at go-live and once the new system is in steady state. Usable data will lag the go-live and this gap, however long, needs to be understood by anyone hoping to use the data to make decisions. In some cases, an interim solution will be required, especially if the data is used for real-time decisions impacting patient care, regulatory requirements, or high-priority fiscal decisions.
If there are 35,000 decisions to be made each day these 6 considerations when implementing a new system serve as a good starting point to enable data-driven decisions.
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