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How to manage healthcare data overload

Healthcare managers are both dependent on and overwhelmed by data. How do make sense of this data overload? pharmaceutical companies are arming themselves with data strategists, data scientists, and Artificial Intelligence (AI) professionals. They are charged with presenting and analyzing the two or three key measures. Leaders can base their strategic decisions on them. This is no small task, and it requires more than just data skills to solve it.

The HBR identified 5 steps to how pharmaceutical companies can create an effective data strategy? A strategy that delivers the right data in the right format to decision-makers. The key aim is to translate information into insights and eventually into action.   

1. Know your data consumers

Begin with the end in mind: identify the end-users of the data. What is the value they are going to gain from the analysis. Based on that, you can segment them according to their seniority and business needs. 

Tip: Try different segmentations that the organisation structure wouldn’t inherently offer. Newly emerging data needs don’t necessarily reflect the organisation structure established decades ago.

2. Understand how end users can make value from data

You want to evaluate how the various customer segments will gain value from the analysis. Value creation from data starts with determining KPIs.Then, consider the current level of performance and progress tracking. At last, set goals and priorities for each of your customer segments within the organisation. 

Tip: Don’t try to think like the end-users: involve them instead as early in the process as possible. It can be done in a form of workshops, 1-to-1 interviews, or cohort exercises. You will not only get first-hand information about their data needs but also early buy-in from key stakeholders.

3. Integrate data sources

Data – just as much as processes – are often siloed within an enterprise. Integration is necessary to get a holistic view of the industry, patient experience and healthcare professionals’ needs. This is the most comprehensive step that requires the most substantial resource investment. However, there are more and more solutions using natural language processing (NLP), ML and AI that can enhance integration and data quality. 

Tip: Instead of using data solely gathered by the organisation, include external data sources as well to gain a 360-degree perspective.

4. Identify priorities

After your initial stakeholder mapping, you will end up with several distinctive requirements, especially if you are working in a heavily matrixed organisation. The goal is to establish priorities and use cases that can further unpack key measures and best practices of data analysis.

Tip: Choose business-critical use cases to get leadership teams engaged. 

5. Translate the data to the users

Find the most suitable format that the data consumers can use in their line of work. Presenting and visualising data in the right format saves time for healthcare leaders. Visualising healthcare data supports them in uncovering key trends and information that will guide them in making decisions.

Tip: Map the existing visualising tools (such as Google’s embedded data application platform, Looker) before you build one from scratch. Even though they still require integration work, they might save you time and resources. 

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Conclusion

Several data sources, different data formats, and a mix of structured and unstructured data can be daunting. Consider it that basing business decisions on a single data source is far riskier than investing in a sound data strategy. 

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