The healthcare industry is experiencing an unprecedented influx of complex data, which can be crucial for the revolutionary improvement of care globally. Meanwhile, BI tools can help turn this data into actionable insights. A comprehensive healthcare business intelligence solution will unlock the full potential of data accumulated within and beyond your organization.
Healthcare BI types and components
Healthcare BI aggregates and analyzes different types of healthcare data, including traditional records such as medical history and financial data and unstructured data gathered from around the web and the internet of medical things. This data is not limited to care providers’ settings but can also come from outside their organizations.
BI software spans four categories:
- Data integration software: tools to acquire data, transform it, if needed, and store it into a repository.
- Enterprise data warehouse: a foundation where data is normalized, stored, and managed across a healthcare facility.
- Data visualization and reporting tools: the representation layer of data analytics, featuring multiple visualization techniques.
- Data discovery tools: allow business users to search data via queries.
The above-listed tools are used in BI solutions to support the following types of healthcare analytics:
- Descriptive analytics: for analyzing historical data to uncover past trends. One example is presenting recent flu outbreak statistics for different age groups.
- Prescriptive analytics: for recommending a specific course of action. For example, how to respond to a patient’s particular symptoms.
- Diagnostic analytics: for understanding the root cause of a specific event. It discovers relationships and causalities. For example, it can find a connection between obesity and heart disease.
- Predictive analytics: for spotting trends and predicting which events are likely to occur. For example, predicting the probability of successfully treating a population segment with a particular medication.
Business intelligence fits perfectly into healthcare
BI can contribute to patient satisfaction, improve health outcomes, and increase the efficiency of healthcare organizations in several ways. We will explore the most popular use cases, although many more niche areas of BI application exist in clinical settings.
Every doctor can attest that there is no one-size-fits-all treatment, and most treatments are never the same for different patients. The same medicine might work well on some patients but do nothing for others. It would be impossible for a healthcare practitioner to manually sift through all the relevant patient data to come up with a personalized treatment.
BI can analyze massive data sets and extract hidden correlations automatically. This can help practitioners customize treatments based on every patient’s specific needs.
Aiding in triage
One of the most complicated processes in healthcare is triaging incoming patients and deciding whom to admit and whom it is safe to discharge.
Unnecessary hospitalization leads to the wasted time of medical emergency staff and hospital beds taken from those who could have needed hospitalization more. Additionally, it results in unnecessary expenses borne by patients, insurers, and hospitals. At the same time, if the staff fails to admit a patient needing continuous monitoring, this can result in complications and even death.
Business intelligence tools geared for predictive analytics and software solutions for healthcare business process management can rank patients’ cases and thus assist in triage.
Predicting patient risks
BI-based predictive analytics can forecast which patients are more exposed to catching or developing a disease. Forecasting capabilities allow earlier intervention and prevention before a condition becomes too severe.
According to the Society of Actuaries report, nearly 75% of healthcare expenses come from only 17% of patients. The ability to identify patients at risk and take preventive measures is what can eventually reduce treatment costs.
Traditionally, healthcare providers estimate the risk using factors such as blood pressure, glucose level, age, and medical history. Now, thanks to the development of fitness wearables and other connected devices, it is possible to incorporate factors such as diet, health habits, and employment conditions and monitor patients in real time.
Unplanned hospital readmissions tend to be the costliest service in healthcare. Readmissions affect patient satisfaction and increase the risk of adverse outcomes.
Many factors can contribute to readmission, and it is not always about incorrect treatment. For example, patients can fail to schedule a follow-up appointment, may not afford medication, or just fail to follow lifestyle recommendations.
BI tools can consider all those factors when predicting risks and helping resolve them without the need for readmission. For example, UnityPoint Health in Iowa has been using predictive modeling in a three-year pilot project. As a result, their readmission rate dropped by 40%.
Why are providers reluctant to employ BI?
While bringing many benefits, BI poses new risks that healthcare organizations need to be aware of:
- Data capturing and storage: as BI is data-intensive, healthcare practitioners have to combine patient care and data entry during the standard appointment time. Data storage is another potential risk. If data is compromised, patients will lose trust in this medical facility. People are already reluctant to allow medical facilities to store their data. For example, over 1 million patients have opted out of the Australian government’s initiative of moving toward an electronic health record system.
- Moral struggle: people tend to be willing to take a riskier action when they feel there is a safety net (viewing BI as responsible for particular outcomes). To avoid such situations, it should be clear who and when is responsible for the final decision. Medical staff should acquire new skills to deviate from a BI-generated recommendation and use human judgment.
Additionally, some leaders believe that their organizations do not have enough data to implement a BI solution. However, predictive models can be created with less-than-perfect data.
Benefits outweigh the risks
Despite all the concerns, BI opens valuable opportunities for healthcare organizations to improve care quality and increase patient satisfaction through properly utilizing the now-available large volumes of health data. However, BI benefits are not limited to interactions with patients but include hospital operations, supply chain management, and staff allocation. Therefore, there is no excuse to pass on such a valuable technology, though it has to be implemented with care and due diligence.