Healthcare Data Analytics: What is It?

Healthcare Data Analytics

Healthcare data analytics is a term that is frequently used to describe the collaborative efforts of healthcare professionals to collect information and analyze it in a way that can provide actionable quality improvement measures. There are four major areas of data that inform healthcare analysis activities: claims and cost, pharmaceutical research and development, clinical, and patient behavior and sentiment data. Taking raw data and converting it into usable formats tends to occur in three stages: data capture, data provisioning, and data analysis. We’re going to focus mainly on analysis since that stage most closely relates to healthcare data analytics.

 

The data analysis step has several components and objectives, each of which is critical to ensure quality and accuracy. First, researchers must conduct a quality evaluation to ensure the integrity and accuracy of the data at hand. Second, they must conduct data discovery so they don’t miss trends or patterns that could be overlooked if they look at the data with too narrow a scope. Third, the data must be interpreted, and finally, the data must be presented.

 

In this article, we’ll look at the data segments that most frequently inform data analysis, examine the steps of analysis, and briefly discuss the implications that good healthcare data analytics has on the industry and on patient care.

 

Data Sets That Inform Healthcare Data Analytics

 

Any information is good information, but certain forms of data provide more actionable insights. There are four main data sets that inform data analysis in the healthcare industry

 

Claims and Cost

 

All-Payer Claims Databases help inform healthcare data analytics processes about costs, waste, and spending.

 

Collecting information on the payers in the healthcare system can help analysts develop better systems that can work to lower the costs of healthcare for both practitioners and patients. All-Payer Claims Databases (APCDs) collect large amounts of data from a wide variety of sources including children’s health insurance companies, private health insurers, Medicaid, dental insurers, prescription drug plans, and the like. The goal of APCDs is to create better quality improvement and cost containment solutions. While many states have been slow to adopt APCDs, they are becoming more popular as their benefit is more widely understood.

 

Pharmaceutical and Research & Development

 

Pharmaceutical research data contributes to healthcare data analytics.

 

Pharmaceutical research is undeniably one of the most important activities contained within the healthcare system. Creating better pharmaceutical solutions can enhance patient care and help more people live longer and more fulfilling lives. The data that pharmaceutical companies and research and development branches provide is a critical component of healthcare data analytics. As more and more patients present with chronic and multiple diseases and illnesses, this branch of research becomes more important and relevant, and the data provided by those seeking to find cures for these diseases can inform patient care and even contribute to predictive analysis efforts. This data also helps policymakers determine the impact of proposed laws on pharmaceutical innovation and research efforts.

 

Clinical

 

Clinical data is a major contributor to healthcare data analytics.

 

Clinical research is probably what most people think of when they hear “healthcare data analytics” or “healthcare research”. This branch of research is devoted to understanding the origin, epidemiology, and impact of diseases, ailments, proposed treatments, and other aspects of the healthcare field. Clinical researchers rely heavily on proper statistical analysis and interpretation of data, and their work, when contributed to big data sets, can inform not only healthcare data analytics but efforts toward quality improvement, AI development, machine learning processes, and predictive analysis systems.

 

Patient Behavior and Sentiment Data

 

This set of data is probably the easiest for most people to understand. Simply put, patient behavior and sentiment data tell researchers how the general public feels about the healthcare system and how they act in relation to it. If you want to study how often people go to the doctor, when they’re likely to avoid going, who’s most likely to avoid seeking care, and whether or not the public at large harbors positive or negative feelings towards the system, this is the area of data you’d turn to. It’s easy to see why a combination of these sets of data would help to contribute to the overall understanding of the healthcare system, its practitioners, its patients, and its effectiveness.

 

The Steps of Healthcare Data Analysis

 

Though data analysis is part of a multi-step process, the analysis process itself also has multiple steps that have to be undertaken in order to achieve proper and accurate results.

 

Quality Evaluation

 

Quality evaluation is an important part of the healthcare data analytics process.

 

It’s important for researchers to determine the quality of the evidence they’re looking at. Big data is extremely helpful to this end because it provides mass amounts of data from multiple sources. This helps to mitigate the chances of data corruption, obtaining incomplete data, or obtaining biased data. Quality evaluation is a critical step that informs all subsequent analysis activities. Ensuring that the data researchers are studying is accurate, free from tampering, unbiased, and soundly acquired sets a foundation of trust and integrity for the rest of the healthcare data analytics process.

 

Discovery

 

The data discovery portion of the healthcare data analytics process can lead to unexpected results and solutions.

 

Going into data analysis with an end goal isn’t necessarily bad, but tunnel vision is. It’s easy to get caught up in the process of attempting to confirm or refute your core hypothesis. However, if you approach data analysis with too narrow a focus you can miss trends, oddities, anomalies, and patterns that are staring you right in the face. That’s why the data discovery process is critical. During this process, researchers take the time to sit with the data and study it without any goals, objectives, or preconceived notions. They look for strange patterns, interesting markers, trends, and other important information.

 

Interpretation

 

Once researchers have qualified the data and looked for any unintended discoveries, it’s time to interpret the data they have. While most people think of interpretation when they think of data analysis, this step of the process tends to take the least amount of time. It’s intensive work, to be sure, but it’s not as tedious and time-consuming as the other stages of the data analytics process. As mentioned in the abstract of Big Data Analysis in Healthcare, “The interpretation of results must be done cautiously by experts who come from diverse fields of expertise and have a wide variety of perspectives.”Once the data has been interpreted and researchers have confirmed their results, it’s time for perhaps the most important stage of the process: presentation.

 

Presentation

 

Data presentation is far more than bells and whistles. The presentation is what can make or break the entire study. During this stage of the process, it’s up to the analysts to use the data they’ve gathered and interpreted to tell a compelling story that will paint a picture of the situation at hand and motivate their core audience to take some kind of action. Sometimes, presentations can be made to obtain funding for research or development. Other times, data presentation can help make the case for funding, treatment approval, structural or organizational change, technology development, or other important objectives. Being able to clearly demonstrate why successfully obtaining the desired result will be cost-effective and beneficial enough to go through with is the make or break moment of all the hours, years, and research that went into obtaining and analyzing the data behind the objective.

 

Implications of Healthcare Data Analytics

 

Depending on how you look at it, healthcare data analytics can have a wide range of implications for any number of the various subdisciplines that healthcare consists of. However, there are several areas that are already seeing dramatic improvements thanks to healthcare data analytics, and these areas show the most promise for present and near-future solutions.

 

Better Customer Service

 

One of the many implications of quality improvement that healthcare data analytics has to offer is in the area of customer service.

 

The data we can obtain through proper analysis can help professionals create solutions that will ultimately lead to better customer service. For example, data trends that accurately predict when hospitals will have the most patients can improve staffing efforts, thus making it possible for shift managers to employ the correct number of people at any given time. This will shorten wait times, ensure enough people are available to provide care, and help improve customer service.

 

Decreased Costs

 

More accurate and efficient solutions mean less waste. Less waste inevitably drives down costs of operation across multiple levels of healthcare facilitation. These lower costs, when transferred to the patient, mean more affordable care. When the fiscal barrier to healthcare is lowered, equity becomes more of a possibility and opens the doors to quality healthcare to many who may not have had it before.

 

More Accurate and Efficient Care

 

Healthcare data analytics can lead to better and more efficient care practices, improving public health and patient trust in the process.

 

Better data analysis means a better chance at providing accurate and efficient end results for patients in the healthcare system. Being able to utilize data effectively means technology, treatments, and other resources can be developed that will help physicians and care providers do their jobs. Faster and more accurate decisions can be made when the decision-makers have the best information possible at their disposal when they need it. Healthcare data analytics seeks to empower healthcare providers so they can make sure patients have the help they need when they need it regardless of contributing or mitigating circumstances.

 

Patient Empowerment

 

Sometimes, the end result of data analytics is a system or service that empowers patients, such as educational products. Whether it’s providing patients with information about their health, teaching them how to access healthcare, or making it possible for them to provide accurate and timely data with which they can obtain a higher quality of care, patient empowerment is one of the most rewarding benefits of data analytics. More educated and empowered patients can better discuss their health and, as such, can obtain more efficient and effective care, and that’s the goal of the entire healthcare system.

 

Quality Improvement

 

Finally, and perhaps most obviously, general quality improvement across all facets of and entry points to the healthcare system is the ultimate goal. Consistent quality improvement leads to developments that behoove every participant in, provider of, and recipient of healthcare products and services.

 

Conclusion

 

Healthcare data analytics is essential to uncovering actionable information that healthcare professionals can use to create more complete solutions. Proper data analysis is a complex process that requires time and multiple systems of error-proofing. Data discovery allows scientists to spot trends and make connections that might be missed if the process is rushed. The end result of well-executed healthcare data analytics is improved technology, better public health, lower costs, more efficient and accurate care, and patient empowerment.