Different Types of Data and their Importance to Heart Failure Readmissions


A healthcare organization can use data to help reduce its Heart Failure Readmissions. This depends on the kind of data and how it’s used. With the continued rise of technology and the use of big data and an increased role it continues to play in healthcare, the industry is finding more and more ways to use data to solve its problems. Below we discuss different kinds of data that can be used in a healthcare organization.

Claims Data

Claims data is standardized and structured data which makes it easily accessible and reachable. Claims data is more often than not, complete data and includes patient demographics, hospital codes like diagnosis codes, general data like dates of services, dates of Heart Failure Readmissions, patients who experience these readmissions and a whole lot more data. Claims data helps with billing and insurance problems and helps make the process a lot smoother and more trustworthy. For example,  Claims data helps providers leapfrog interoperability issues that prevent them from accessing complete and longitudinal clinical data about patients from external organizations and other related organizations along the care continuum. This is a great advantage because the healthcare industry is very huge and ever-growing and organization overlap all the time in their operations and billings. Say a patient receives treatments in one organization and is then transferred to another for a different treatment process, the claims data would include these changes and every billing that occur during and after the process.

As with everything great and wholesome, there are bound to be some limitation and the same is true for claims data. Claims data can be retrospective which means it does not include some important clinical details that provide better insight into the process of care. Claims data essentially only contains billing information and any other information that’s directly relevant to billing. This makes it limited in its own way. Thus, while claims data provide information and insight into the state of patients, it cannot fully show a proper representation of the medical and health state of a patient and subsequently, the population health.

Electronic Health Records

Electronic health records is one of the more commonly known types of data in that it is widely used by most healthcare organizations in the industry. Electronic health records provide more whole data that include lots of the clinical and treatment information that’s not included in claims data. For example, an Electronic health record will contain details about the process of care that a patient has received and is receiving. And this includes everything from diagnosis, any procedures being done, medication, patient allergies, past medication taken, past procedures received, and information about any patient care team assigned to them – if that’s the case,  lab results, etc. Because of this, patient care providers can answer a lot of questions about a patient just by looking at their data stored in the electronic health records. Answering these questions are important because they give healthcare providers, doctors, and nurses, a lot of information which can be applied to population management.

 

One problem with electronic health records is that data entry might be rushed leading to the data having weak integrity. Users may take shortcuts when inputting the data and might result in just copying and pasting the data or making errors while inputting the data. This is because doctors and nurses when in a hurry, find that inputting the extensive data that an electronic health record requires takes up a lot of time and manpower and so try to take shortcuts to fix it. The main drawback to this is its effects on population management. Population management needs data and when this data is incorrect or untrustworthy, it delays research activities.

Patient Generated Health Data (PGHD)

Patient Generated Health Data is as the name implies, data gotten from patients in the form of patient-reported outcomes, feedback forms, satisfaction surveys and more recently data from wearable health devices worn by patients. In the last decade, there has been a rise in the role Internet of Things (IoT) powered technology in healthcare such as wearable fitness trackers. This can be attributed to the availability of this technology and an increased interest in fitness and good health by the general population. The availability of this technology has led to more individuals being interested in teaching their health and doing more physical activities in order to meet their health or exercise goals. A pedometer counts steps and increased use of a pedometer by people has led people to be more active, walk more, go out and exercise more so they can meet their step goals and even surpass it. This means that there is also more data available for the healthcare industry. Many argue that Patient Generated Health Data is one of the most accurate data that shows the true state of the population health because it is generated by the patients themselves and there is little room for guessing or for errors.

Thus, the healthcare industry especially those in the Electronic Health Records space are currently looking for ways to integrate patient-generated health data with electronic health records and other new software products. For example, while some high-risk patients may benefit from around-the-clock monitoring, most individuals will not require constant attention to their sleep patterns, heart rate data, and exercise regimes in order to remain healthy and as such the merging of patient-generated data and electronic health records will lead to both data needs to be met seamlessly. This would eventually lead to better data gathering and storage and a better data culture which will help solve problems that plague healthcare organizations such as heart failure readmissions.

 

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