COVID-19 affected risk scores and quality outcomes: Here’s what you can do about it

As value-based payments continue to grow, provider organizations and health plans are relying more and more on predictive modeling of risk scores and quality outcome measures as a driver of financial performance. To do that well, risk and quality measures require data based on individuals using the healthcare system to manage their medical conditions and prevent illnesses. Several studies published last summer projected Medicare Advantage Hierarchical Condition Category risk scores could be 2% to 3% lower due to a decline in utilization from COVID-19 public health restrictions.

The question healthcare finds itself asking is—How does the pandemic impact risk scores and healthcare quality outcomes? And, most importantly, can it trust 2020 data to make operational, financial and clinical decisions in 2021 and beyond?

Pandemic impact varies by region

Most of the impact on healthcare utilization occurred from March-May 2020 when expenditures were 40% lower than the prior year. This isdue to patients delaying care and the limitations placed on elective procedures. The volume of healthcare services recovered substantially during the second half of 2020. By the second month of 2021, hospital admissions, physician visits and professional services had rebounded to 2019 levels resulting in 2020 annual admissions and visits at 4% below prior levels.

The impact of COVID-19 on healthcare utilization varied across regions of the country depending on the intensity of the infection rate and the extent of public health restrictions imposed by state and local governments. Northeastern and western states had the largest drop in admissions and visits during 2020 while the south central region had the lowest utilization declines of any region.

So, can you rely on 2020 risk scores to use in value-based payment calculations going forward? Without a solid data analytics solution in place, the answer is a definite “maybe.” For those healthcare organizations that do rely on a structured data analytics solution, you’re on solid ground to make strategic decisions.

Analyzing your plan’s key medical conditions and healthcare services per 1000 member trends can provide insight into the potential impact of key drivers on risk score results and quality outcomes in your enrolled population.

Start, stop processes with analytics

If you’re unsure of where to start—whether you have a data analytics solution in place or not—these ideas will help.

Concurrent or retrospective scores are calculated using members’ age, gender and diagnoses. Cost and utilization are not incorporated thereby permitting scores to risk adjust providers’ average costs and utilization rates.  Prospective, or predictive, risk models weigh more heavily a member’s recent costs and utilization along with the same diagnosis and demographic factors used by concurrent models.

Concurrent risk scores are impacted most by:

  • Health plan enrollment shifted as some people changed health coverage due to layoffs and business failures. Compare the distribution of members for 2020 with prior years and current enrollment to see how higher-cost segments may have changed—people 45-64 and 65+ and lower-cost groups such as dependent children. Another option is to review age/gender risk index trends for the same periods.
  • A single inpatient admission typically averages 8-10 diagnoses. Admission trends should be analyzed by service as medical/surgical admissions are associated with higher risk scores than admissions for maternity and mental health. Elective surgeries were estimated to have been reduced by 15% in 2020. Admissions of Medicare patients declined by 80% more than other payer sources.
  • High-cost conditions, such as cancer, renal dialysis and strokes can be analyzed using trends in members/1000. Risk score bands may show stable or increasing numbers of members in the highest risk band as they are generally the same people at higher risk of complications from COVID-19. IQVIA estimates the volume of cancer cases in 2020 was consistent with expected cases.
  • Prescribed drugs may offset the potential of fewer diagnoses due to delayed care. Prescription drug spending in 2020 was similar to 2019 levels.

Chronic health conditions are another major driver of predicting future healthcare costs and utilization. You’ll want to analyze these numbers to determine if any trends may be lowering your plan’s prospective risk scores.

  • Changes in chronic condition incidence may be affected by trends in ambulatory, pharmacy or laboratory claims per 1000 because 70% of chronic conditions are identified by these claims. Ambulatory care visits, lab testing and prescriptions—often associated with chronic conditions—rebounded to pre-COVID levels by the end of 2020.
  • Telehealth visits accounted for more than 15% of all physician visits in 2020. Telehealth can offset in-person office visits and should be part of every physician’s treatment process.
  • Changes in compliance with evidence-based guidelines related to managing chronic health conditions are a good indicator if predictive risk scores are understated or consistent with prior years.
  • Preventive services can increase prospective risk scores as they identify or protect against potential higher-cost conditions. An estimated 16% of preventive cancer screeningand immunizations were missed from March-May in 2020.
  • Specialty drugs used to treat rare, chronic medical conditions drive higher prospective risk scores. Specialty drug therapies increased by 10% in 2020, likely increasing your risk scores.

What if you conclude your health plan’s 2020 risk scores could be understated due to declines in some of these key utilization trends? It’s difficult to recalculate prior risk scores to eliminate the potential impact of COVID-19, but you may be able to modify how risk scores are used.

We can’t undo business disruptions caused by the pandemic, but we can continue to leverage healthcare data from 2020 going forward to operate effectively in the “new normal” as our economy recovers and more patients return to healthcare. When you lean on data analytics to do the heavy lifting to predict utilization and cost trends, moving forward becomes much easier and the potential outcomes—financial, operational and clinical—more reliable.

Rob Corrigan

Rob Corrigan has over 30 years of working with senior executives at insurance plans, healthcare providers, government payers and national employers to design advanced analytics and prescriptive models to manage cost trends, population health, payer-provider partnerships and benefit plan designs. He is a strategic advisor to payers and provider organizations on leveraging their data to produce actionable insights using predictive modeling, assessing quality and financial outcomes.

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