How to manage healthcare data overload
Healthcare managers are both dependent on and overwhelmed by data.
They are facing a significant challenge in dealing with the massive amount of data generated in the industry. While healthcare organisations use data strategists, data scientists, and Artificial Intelligence (AI) professionals to analyse the key measures, leaders require more than just data skills to solve this problem. A robust data strategy is necessary to deliver the right data in the right format to decision-makers, enabling them to translate information into insights and eventually into action. This article will discuss the five steps to creating an effective data strategy for pharmaceutical companies to manage healthcare data overload.
The HBR identified 5 steps to how pharmaceutical companies can create an effective data strategy, and we added helpful tips to each of the steps. The aim is to translate information into insights and eventually into action.
1. Know your data consumers
The first step in creating a data strategy is to identify the end-users of the data. It is crucial to determine the value they will gain from the analysis and segment them based on their seniority and business needs. The segmentation can be different from the organizational structure established decades ago, as newly emerging data needs may not reflect the old structure. Therefore, it is essential to try different segmentations to understand the data consumers’ needs accurately.
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
Once you have identified the data consumers, the next step is to evaluate how they will gain value from the analysis. This step begins by determining Key Performance Indicators (KPIs) and then considering the current level of performance and progress tracking. Finally, set goals and priorities for each of your customer segments within the organization. Involving the end-users in the process as early as possible is essential. This can be done through workshops, one-to-one interviews, or cohort exercises to get first-hand information about their data needs and early buy-in from key stakeholders.
Tip: Don’t try to think like the end-users; involve them 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 get first-hand information about their data needs and early buy-in from key stakeholders.
3. Integrate data sources
Data and processes are often siloed within an enterprise, which makes integration 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), Machine Learning (ML), and AI that can enhance integration and data quality.
Tip: Instead of using data solely gathered by the organisation, also include external data sources to gain a 360-degree perspective instead of relying solely on data gathered internally.
4. Identify priorities
After your initial stakeholder mapping, you will end up with several distinctive requirements, especially if you are After the initial stakeholder mapping, you will end up with several distinctive requirements, especially if you are working in a heavily matrixed organization. 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 fully engaged.
5. Translate the data to the users
The final step is to find the most suitable format that the data consumers can use in their line of work. Presenting and visualizing data in the right format saves time for healthcare leaders. Visualizing 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.
Conclusion
In conclusion, creating an effective data strategy for healthcare organizations requires a thoughtful and methodical approach. By following the five steps outlined above, pharmaceutical companies can create a data strategy that delivers the right data in the right format to decision-makers. The end goal is to translate information into insights and eventually into action. While it may seem like a daunting task, investing in a sound data strategy can ultimately lead to better decision-making and improved patient outcomes.
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