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How to increase data accuracy in healthcare

Updated: July 21, 2021 Posted:
Healthcare Analytics Data Interoperability and Integration Data Management and Quality Unlocking Big Data

Data plays a crucial role in driving informed decision-making and improving patient outcomes. However, ensuring the accuracy of data in healthcare settings can be a complex and challenging task. By understanding the factors contributing to the disparity in data accuracy perception and implementing strategies for educating stakeholders, healthcare organizations can enhance data accuracy.

This article explores key insights from Unlocking Big Data, Episode 3 by industry experts Anne Snowdon, Chief Scientific Research Officer at HIMSS, and Mary Kuchenbrod, VP of data operations at Arcadia. In this episode, they discuss the nuances of data accuracy, strategies for educating stakeholders, and steps organizations can take to enhance data accuracy.

Understanding the disparity in data accuracy perception

According to Anne Snowdon, "It's a question of fit for purpose. Executives are pretty confident in their data because the data they need to make strategic decisions is vastly different than those clinicians." Snowdon highlights that financial data is usually accurate and easily captured, while clinicians deal with more complex and contextual patient data, leading to a lower perception of accuracy among clinicians.

Mary Kuchenbrod adds, "Data is really messy in healthcare. Financial data is much cleaner." She further explains that data in healthcare requires active curation to be usable, and the effort put into data preparation often shapes the perception of data quality.

Educating stakeholders on data accuracy expectations

Stakeholders should have clear expectations of the data's transformation and lineage to make effective decisions. Kuchenbrod suggests, "You have to start with what type of data is needed. Then from there, it’s about timelines.” Kuchenbrod emphasizes the importance of understanding the specific data needs for different use cases and considering the timeliness of data delivery.

Snowdon highlights the need to educate stakeholders about the organization's analytic strategy. She states, "Where are your analytics and data strengths? Where aren't they?" By providing stakeholders with a transparent picture of data assets, gaps, and limitations, organizations can align decision-making processes with available data.

Overcoming data lifecycle challenges

Organizations need to consider the analytics lifecycle. Understanding the flow and accessibility of data within the organization is critical for data-driven decision-making.

Kuchenbrod emphasizes the importance of data aggregation and analytics, saying, "Data aggregation is not a commodity. It takes a deliberate strategy and an execution plan and an implementation model and a governance model." Kuchenbrod further highlights the significance of data curation and management, stating that these aspects are critical for putting insights at the point of care.

Steps to enhance data accuracy

To enhance data accuracy, organizations should start by assessing their current strengths and strategic priorities. Snowdon suggests, "Start from a position of strengths.” Organizations must identify areas for improvement and align data and analytics capabilities with their strategic objectives.

Kuchenbrod emphasizes the need for a deliberate data aggregation strategy. She states, "Both on the side of data aggregation and analytics, there’s a much more nuanced understanding of what it takes to achieve data accuracy." Effective data management, curation, and automation are key to ensuring data accessibility and usability. By taking these steps, healthcare leaders can unlock the potential of data and pave the way for data-driven healthcare transformation. View more of the Unlocking Big Data series presented by HIMSS.

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