Traditionally, the US Healthcare system relied on Fee for Service (FFS) care and patient volumes to keep up the financials intact. With the Covid-19 pandemic, elective surgeries were interrupted, chronic disease treatments were postponed, and hospital visits dropped by more than 70%. Burdened by the influx of Covid-19 cases and excessive staffing requirements, the Healthcare system got crushed financially too. The system also began to show the flaws of the chunky FFS model and more power to Value-based care.
Value-based care provides incentives for quality and FFS for quantity. Medicaid and ACA also have been pushing for Value-based care for quite some time. Quality of care is not just about the required treatment. It is about patient experience, lifestyle management, post-treatment experience, preventive care, and population health management. Data analytics and predictive modeling turn out to be competitive differentiator for Value-based care.
Cost management, claims management, and revenue cycle management become important in the post-pandemic ‘New Normal’ era to help organizations rebuild their fiscal health. Hence, financial analytics can only ease the transition to value-based care and enhance the fiscal health of the system.
Catalysts for Financial analytics adoption
Market adoption – As per the latest MarketWatch report, the Healthcare Financial analytics market is expected to grow at a CAGR of 24.5% during 2018-23. Factors such as growing federal pressures to curb costs, preference for telemedicine, Electronic Health Records adoption, initiatives focused on quality of care, and personalized medicine/treatment plans, are expected to drive growth.
Analytical tools are under-utilized – As per research from Black Book Market research in 2020, 93% of C-suite executives believe that data analytics is “crucial” to future healthcare demands/operations. At the same time, 84% of them labeled the usage of advanced analytics at their organization “negligible”.
Most often analytics were used to justify past decisions and not for strategic planning. There is a shift to identify key recommendations for growth with the new changes through the pandemic.
Lots of data with only a few right answers – There is a huge pile of data, but most of it goes unused. Nearly 70% of all-payer data and 90% of all data is not used. It is collected and stored — but never organized, interpreted, or operationalized.
A few Use cases
1. A closer look at margins and costs by service line, procedure, treatment plan, and per patient
It is always an overall view of the costs and margins and they were not looked at in each granular detail. The pandemic has shown the importance of effective utilization of healthcare resources while also managing costs. A patient-to-patient comparison and procedural comparison can give insights about similar cases in the future and strategies to optimize costs. A particular set-up may not be profitable in all the service lines and procedures.
2. Predictive analytics to identify at-risk patients for deferred or canceled treatment
Coronavirus has delayed care for many people and still, the fear of entering a hospital set-up is back holding many treatments. A risk analysis of certain chronic diseases (Cancer, Heart failure) and behavioral health issues can aid in prioritizing the care plan for the most needed.
3. Revenue cycle analytics
Forecasting revenue and estimating demand are key to managing the entire healthcare system efficiently. Further decisions like staff planning, supply chain management, and operational planning are dependent on this. Also, these insights can aid in proactively managing delayed payments from payers. In an uncertain time like this, it is much needed to be calculative for the future.
Apart from these, there are others like claim analytics, readmission risk, Occupancy analytics, and a few more to aid in financial decisions.
Challenges for adoption
· Limited data management – The healthcare industry is still not ready for a massive inflow of data. A lot of manual processes exist in collecting and processing the data. The lack of a unified data view for everyone in healthcare organizations is also a hindrance.
· Legacy Systems – Cloud adoption is still underway. A lot of organizations still grapple with legacy. As per the Association of International certified professional accountants, 59% of healthcare organizations have difficulty extracting data from legacy systems.
· A new technology trend evolves rapidly. But skills needed to implement the change are scarce. It is the same with analytics implementations too.
Solutions vs Service Providers
Deriving insights can be similar to both services and solutions providers. Large organizations with robust healthcare management systems may find it easy to adapt to big solution providers like Oracle, IBM, Optum, etc. You can find a lot of small solutions and service providers in the market for Healthcare analytics. In case you can find your best product fit, solution providers can be a good choice. Service providers currently dominate the market. A service provider with consulting experience can craft a roadmap and ideal solution according to your requirements. Customized needs and agility can be easily addressed by niche service providers.
Technology adoption is accelerated with the pandemic in every sector. Healthcare is already facing challenges with respect to quality and costs. Covid-19 has worsened it. Leveraging technology and data is key for healthcare to be vital.