A Prescription for Improving Patient and Institutional Financial Wellbeing
The Accelerating Importance of Data Analytics in Healthcare – Part 1 of 5.
Data is the lifeblood of the next-generation health system, and innovative use of analytics will drive better outcomes and lower costs. Data analytics tools have the potential to transform health care in many ways. The inpatient setting will be improved by more sophisticated insights drawn from an ecosystem of interconnected health data. The care patients receive may be better decided in consultation informed by expert judgments but also by algorithms that draw on information from patients around the world. Diagnosis and protocols may be customized for an individual’s personal genetic information, enabling doctors and nurses to use more advanced ways to diagnose, track, and treat illnesses.
However, compared to other industries, the slow pace of innovation reflects challenges that are unique to health care in implementing and applying modern data analytics techniques. These barriers include the complexity of health care decisions, sensitive and messy data, historical practices in care delivery, and the misaligned incentives of the participants in the health care delivery supply chain.
Nevertheless, advanced analytic insights have quickly become an integral part of the overall strategy for almost all organizations across many industries. Organizations have started to rely heavily on the insights gained from their data ecosystems to stay competitive within the market. The health care industry is no exception. This includes hospitals, pharma, and insurance companies. In this blog, we will look at some examples of how the health care industry has leveraged advanced business analytics tools to gain valuable insights to improve not only their bottom-line but also patient outcomes. We will also look at the challenges that are keeping many organizations from adopting these powerful new solutions.
Several hospitals are now using analytics to better understand and manage patient visit times using data across multiple years to schedule their staff members’ hours. This directly saves cost by reducing staff levels during times when the patient volume is low. They are also using analytics tools to gain insight into what medical procedures are bringing in the most revenues and adjusting their offerings accordingly. Additionally, they are relying more and more on analytics to keep a pulse on their finances and measure and compare their period-over-period financial performance.
Health Insurance companies are using data mining of patient claims to identify the best providers for a specific health condition. Also, they are heavily using data analytics to gain insights into the health and behavior of their patient population to tailor their wellness programs accordingly. This provides a win-win situation, as it not only helps the insurance companies lower their costs but also improves patient outcomes.
The emergence of advanced analytics has also accelerated the growth in healthcare research and development. Predictive Analysis is now being used to predict clinical outcomes within groups of patients. Just imagine if your doctor could analyze millions of records of patient data with a similar background as yours to predict your risk level for a specific disease. You could then use that information to take corrective action to avoid contracting the disease much in advance of being diagnosed with it. Alternatively, better yet, what if this information was available at the time of birth – parents could make informed decisions for their children and adjust their lifestyles accordingly. These tools are certainly going to prove extremely beneficial in allowing people to live longer, healthier, and happier lives.
As we can see, there are many benefits of using advanced analytics within the healthcare space, but it’s not without its share of limitations which pose significant barriers to the access and application of healthcare data within the organization. Based on a recent survey of 102 health care organizations by eHealth Initiative (eHI) and the College of Health Information Management Executives (CHIME), 80% of the respondents believed that their strategic roadmap relies heavily on business intelligence and using big data and predictive analysis. However, 84% of the respondents also found that using Analytics poses significant challenges for their organization. Only 45% of those who responded, have implemented a successful analytics solution. The rest cited a lack of staff training, data governance, and data integration issues for their lack of analytics adoption. In addition, health care regulatory laws such as HIPAA, which govern patient records and imposes strict penalties upon non-compliance, also discourage some organizations from analytics adoption.
So, in conclusion, business intelligence is fast approaching to becoming one of the most critical strategic drivers for health care organizations (or for any organization for that matter) and, as a result, we shall see a significant improvement in the overall public health and well-being in the next few decades.
In my next blog, I’ll discuss the complications surrounding the health care data revolution.