The Importance of Data Literacy in Public Health and Health Information Management
The origins of the quote, “there are three kinds of lies: lies, damned lies, and statistics” has been argued over the years. However, the premise of the quote paints a picture of what we see in society today — statistics, or numbers in general, can be manipulated to produce misinformation. In a world, where we are consumed with data, numbers and statistics, our ability to understand what it means and apply it will become more and more important.
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 your using data to make decisions? Are you using data to promote healthier living within your community? Are you applying recommendations in your clinical practice? Are you using statistical methods to analyze datasets in robust clinical trials?
Data literacy and public health
‘Data’ has plenty to offer to the public health sector, but the industry can only reap benefits if it understands 1) what and how data is being collected, 2) what the data means to its various stakeholders, and 3) if any changes to current practices should be made (and how to make those changes).
Individuals working as leaders in the sector may be interested in rates or trends of different diseases in the region and how to make evidence-based decisions about current practices. Frontline health care workers, like physician, nurses, and allied health workers may be interested in learning about the most effective medications or practices to lower the risk of heart failure in their patients. Community workers may be interested in how to relay information in easily digestible formats to the public, or how to promote specific recommendations at the community level.
While various perspectives in public health may need to answer different questions, they all need to understand how data, in its basic numeric form, is extracted, gathered, processed, and used for analysis and decision-making purposes. Furthermore, the public health workforce must be able to critically appraise what they are reading in order to make 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. For example, medical students are taught about the different types of research studies, how to carry them out, and how to evaluate research articles for its strengths and weaknesses.
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.
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. For example, decision-makers may be interested in preventing hospital-acquired-pneumonia (HAP) at their site because they see rising rates in a particular unit. 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.
The steps involved in collecting and analyzing data will likely continue to evolve for many years. We are currently moving from paper-based formats of data to electronic forms. We are also seeing the sheer volume of data consumption increase across all sectors; including those outside of public health, like sports (i.e. points by game to evaluate how well someone is playing) and education (i.e. how well schools are doing to get funding for programs). Therefore, it is unlikely that the importance of data literacy is going anywhere. The type of data literacy needed, and the in-depth nature of its understanding will be dependent on you and the context in which you plan to apply it.
Written by Aven Sidhu