How to Reach Your Clinical Data Analytics Objectives

Healthcare firms understand the importance of using clinical data analytics to improve and survive in the new, competitive environment of value-based care. To improve patient care, per capita costs, and patient and clinician satisfaction, healthcare administrators must set clear goals for their clinical data analytics efforts. Although this appears to be a simple notion, it is frequently challenging for healthcare organizations to develop.

What Does Clinical Data Analytics Mean?

Clinical data is any information that is a compilation of observations about a patient or a community. Clinical data is classified into six types:

* Electronic medical records

* Administrative information.

* Data on claims.

* Registries of patients/diseases

* Health polls.

* Data from clinical trials.

Clinical data can help an outcomes improvement team revolutionize patient care by increasing health outcomes, saving costs, and improving patient and healthcare professional experiences. Want to know how clinical data analysts use data in the healthcare sector? Visit the online data analytics course and master the top analytics tools used in the real world.

Because of the widespread use of EMRs, healthcare institutions collect massive amounts of clinical data. Unfortunately, the majority of this data has yet to be seen or assessed. To transform healthcare, we must transition from the era of "big data" to the era of "useful big data." Big data with meaning is information that can be used to inform change. Simultaneously, we must begin to question what data is not influencing change and then establish systems to streamline documentation. This is why a strong clinical data analytics approach is required.

According to an MIT Sloan Management Review study, top-performing companies in their respective industries are three times more likely to be savvy users of analytics than lower-performing companies, with a "lack of understanding of how to use analytics to improve the business" being the most significant barrier to leveraging data. Cultivating success necessitates the development of interdisciplinary outcomes improvement teams that are actively engaged in the transformation process by healthcare systems. To succeed, these teams must be led by doctors who are affected by the work and believe in making data-driven decisions.

Three Types of Clinical Data Analytics Strategy Goals

One of the most crucial elements in initiating organizational transformation is goal planning. The drive for every outcome improvement effort must come from a problem that describes the cause for change. This should be a brief statement that clearly identifies the problem and the consequences of not responding. The problem description should not be mistaken for the problem's symptoms, nor should it strive to describe the precise processes necessary for the problem to be corrected.

For example, a community hospital discovered that the incidence of patients leaving the Emergency Department without being seen was greater than it should have been (problem), indicating overcrowding and excessive wait times (symptoms). The problem statement should not be confused with solution-improving wait times in this case.

* The end goal can be defined after the problem statement has been created. Goals for outcomes should be viewed as an extension of the problem description. According to Simon Sinek's work, every company on earth knows what they do; in healthcare, it's taking care of people. Some organizations understand how they operate, but few understand why.

* An improvement team's esprit de corps is formed when they write a problem statement. The desired consequence should be a precise extension of that statement. After identifying the why and its extension, outcome improvement teams can identify the how (process) and describe the what (intervention). Finally, for teams to succeed, the evaluation must be the product of a thorough study. Balance measures enable this enhanced view and provide team members with insight into determining if the various processes and interventions are producing the desired result and that no unintended consequences exist. For detailed information on this, refer to the data analytics course.

* Measures of Success

Outcome measurements, which include mortality, readmission, and variable cost per case, are broad quality measures that healthcare companies are attempting to improve. According to the World Health Organization, outcome measurements are "changes in the health of an individual, group of people or population attributed to an intervention or sequence of interventions." Outcome measures are long-term goals that take the most time to achieve and are frequently influenced by multiple processes. It is also crucial to highlight that outcome measurements in healthcare are frequently surrogate measures as a result of data availability constraints.

When an outcome measurement, such as mortality, is employed, often in-hospital mortality is recorded rather than total mortality - there are constraints to adequately tracking mortality events that occur outside of the hospital or healthcare system.

* Process Metrics

Process measurements are specific statements that are frequently easier to measure and can be completed in less time than their result equivalent. They are tied to accomplishing the result goal and show how important system components are progressing.

For example, if the outcome measure is LOS, a process measure associated with that outcome may shorten the time between when a practitioner issues the order to discharge a patient and when the patient departs the facility. Other process measures include the percentage of patients with sepsis who receive an antibiotic within three hours of their arrival or the percentage of patients with heart failure who have a recorded ejection fraction. These metrics are the specific steps in a process that lead to a given result metric and are crucial in reducing unjustified variation in patient care and thereby improving the quality of care delivered. They employ best business practices to systematize their attempts to improve.

* Measures of Balance

Finally, there are metrics of equilibrium. These can be either process- or outcome-related measurements that help ensure that the implemented changes are moving the needle in the right direction and have no unintended consequences. Balance measure outcomes are almost always influenced by processes that have been put in place. If a clinical team is attempting to reduce the use of emergency department services in their system and has decided to implement a care management program to see if they can reduce patients from using unnecessary ED services, a balance measure could include looking at out-of-network services or inpatient hospitalizations.

When considering balance measures, it may be useful to consider them as proxy safety measures. When considering these actions, make sure to give yourself enough time to ensure the safety of the patient, clinicians, and healthcare systems.

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