The Importance of Data Literacy in Public Health and Health Information Management
By: Aven Sidhu
Published On:
November 15, 2024
U
Data Literacy Overview
The origin of the quote “lies, damned lies, and statistics” is debated. But its meaning is clear: numbers can be manipulated. In our data-driven world, understanding statistics is crucial.
Data literacy can be defined as “the ability to read, write, and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application, and resulting value.” Therefore, what data literacy means will differ based on your context.
Are you using data to make decisions? Using data to promote healthier living within your community? Applying recommendations in your clinical practice? Are you using statistical methods to analyze datasets in robust clinical trials?
Data literacy and public health
Data offers much to public health. To reap benefits, the sector must understand data collection, interpretation, and potential improvements. Leaders seek disease trends and evidence-based decisions. Frontline workers aim for effective treatments. Community workers focus on public understanding and recommendations. Public health professionals must therefore understand data extraction, gathering, processing, analysis, and decision-making. Critical appraisal is essential for evidence-based decisions.
Closing the gap of data literacy education
With the number of research articles available to the public increasing exponentially since the late 1990s, it is becoming more and more important for all public health disciplines to have the knowledge and skills to critically evaluate the strength of their findings.
One study found that for most data literacy skills, a high proportion of respondents indicated that they never had formal data literacy training, but also reported having “medium to low” levels of data literacy expertise (self-assessed). While this is a single study with a small sample size, it suggests that there may be a need for data literacy training. Other surveys have found health care professionals like physicians may be overwhelmed with the amount of health data available.
One solution is to improve how data literacy education is delivered. Much of the data literacy education has stemmed from libraries educating undergraduate students. However, individuals pursuing post-graduate education or entering certain health care/research fields may find their undergraduate data literacy education insufficient. Data literacy education that builds upon previous education and is delivered more frequently to those pursuing higher education may be appropriate.
Another solution is the integration of health information management courses in different health sectors. We see this in some public health fields already.
However, if individuals are not using this education regularly, they may not retain the knowledge years later. Therefore, a third solution is the incorporation of health information management (HIM) within multidisciplinary public health teams.
Looking forward
The profession of HIM is strategically placed to manage the data literacy gap. HIM professionals serve as vital pillars in public health by engaging in data collection, analysis, project management, and understanding the tools (i.e. different databases, statistical software, etc.) used in the various steps. Using their knowledge and education, they can summarize large datasets to create a holistic picture of health information available to inform evidence-based decisions which enhance health outcomes for patients.
Each perspective on a multidisciplinary team – decision-maker, clinician, researcher, patient partner or health information specialist – will provide unique contextual elements that can help shape the understanding of the data. HIM professionals can extract and summarize the relevant data for their site. Clinicians and patient partners can provide specific contextual understandings to the data (i.e. workload or staffing issues). Researchers can use this data to hypothesize new anti-microbials to treat HAP. This is an iterative process that incorporates the four steps of improving the quality of care: Plan, Do, Study, Act.
Conclusion
Data collection and analysis are evolving. We're shifting digital and data consumption is booming. Data literacy is crucial. Your needed skills depend on your goals and context.
Disclaimer
The information contained in this post is considered true and accurate as of the publication date. Ashton College assumes no liability for any error or omissions in the information contained in this post or any other post in our blog.
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