Elective Surgery for Preference-Sensitive Conditions

Is Your Rate Too High, Too Low, or Just Right?

Mar. 16, 2021

Diminished spending on healthcare services in 2020 related to downstream impacts of COVID-19 has left employers and health plans wondering what to expect in 2021. In our Employee Health Trends report, we described a number of healthcare services that were impacted in 2020, including elective procedures.

Reduction in elective procedures through September of 2020 resulted in a savings of over $16.82 per member per month (PMPM) in 2020 compared with the same time period in 2019. Whether those savings will be depleted in 2021 by rates of elective procedures that exceed pre-COVID rates remains an area of interest in financial forecasting.

Diminished performance of procedures for preference-sensitive conditions were responsible for much of the decrease in spending related to elective procedures. In fact, about 15 percent of the savings can be attributed to decreased volumes of three elective orthopedic procedures done for preference-sensitive conditions:

  • knee replacement,

  • hip replacement, and

  • spinal fusion.

Preference-sensitive conditions refer to conditions where there are multiple options for treatment, often without a scientifically proven “best” option. These treatment options have trade-offs in terms of risks and benefits such as:

  • cost

  • likely clinical outcome

  • potential for complications

  • recovery time

Once informed of the risks and benefits of all options, patients can make decisions based on personal preferences. Examples of common preference-sensitive conditions which have medical and surgical treatment options include degenerative joint disease (osteoarthritis), stable ischemic heart disease, and low back pain related to herniated disc or spinal stenosis.


Whose Preference?

While “preference sensitive” is meant to refer to the patient’s preference, available data suggests that treatment for these conditions often reflects the treating physician’s opinion of the best option.

Much of what we know about this issue comes from studies of geographic variation in rates of surgical procedures. For example, the rate of knee replacement surgery has been reported to vary four-fold across different geographic regions in the United States 1.

While regional differences in the prevalence of disease and patient preferences might result in small geographic variations in rates of procedures for preference-sensitive conditions, the magnitude of regional differences suggests that physician opinion plays a major role in determining the selected treatment.


Achieving The “Right Rate” Using Shared Decision Making

If there are a number of treatment options for a condition, how can we determine if the surgical rate in a population is the “right rate”? One possible definition of the “right rate“ for surgical procedures among individuals with preference-sensitive conditions is the rate “that results from the choices of a fully informed and empowered population.”2 The focus becomes less about the actual rate and more about the process to enable patients to make the best decision.

Of course, having patients select preferred treatments isn’t as simple as having them choose a preferred color or food. Because most patients are unaware of the full range of treatment options and their associated risks and benefits, the emphasis has been placed on Shared Decision Making (SDM). SDM is a two-way street. Providers share information on the risks and benefits of various treatment options while patients clarify their preferences and personal situation.

While many studies have shown that individuals participating in SDM are less likely to opt for surgery, others have shown no change, and at least one has actually shown an increase in individuals opting for surgery 3, 4. In addition to patient characteristics and the specific procedure being considered, several other variables may impact the outcome of SDM, including:

  • Who discusses the decision with the patient – Is it someone who is trained in shared decision making? Many physicians have little or no training related to SDM and may have biases regarding medical or surgical treatment depending on their specialty.

  • When does the discussion take place relative to the onset of symptoms or initial visit to a surgeon? Patients may not see the need to consider a range of treatment options at the onset, but waiting until a patient visits a surgeon may be too late to have an impact.

  • How is information about risks and benefits presented? Due to the complexity of understanding risk and benefits, patient decision aids such as pamphlets and videos are often used. When provided ahead of a consultation, patients may be better prepared to ask questions.


The Role of Employers and Health Plans in Encouraging Shared Decision Making

Employers and health plans can provide services and incentives to encourage employees and their families to make better decisions. These strategies include:

  • Empowering employees and families with basic questions to ask5, including:
    • What are my treatment options?

    • What are the benefits and risks of each option?

    • Where can I find more information to help me decide?

  • Providing healthcare coaching services with trained individuals who can discuss options with patients

  • Creating financial incentives for members who use patient decision aids before deciding whether to undergo surgery

  • Using Centers of Excellence to steer individuals to systems that prioritize appropriate utilization


How Springbuk Can Help

Springbuk’s health data analytics platform enables you to find the information you need to understand the recent use of preference-sensitive procedures, identify individuals who may undergo these procedures in the future, and track the impact of programs you implement with a few clicks.


Understand recent use - Springbuk Insights and Springbuk Answers allow you to trend recent use of specific preference-sensitive procedures, determine common characteristics of individuals who selected surgery, measure provider-specific surgical costs, and benchmark use rates.


Identify individuals at risk of undergoing these procedures in the future - Springbuk Insights, powered by data science and complex algorithms, identifies individuals with conditions who may consider surgical procedures in the near future.



Track impact of programs - Springbuk Timeline is a unique feature that allows you to keep track of implemented strategies and visualize impact.

Conclusion

Encouraging providers and patients with preference-sensitive conditions to engage in shared decision-making leads to patient-centered decisions, and has the potential to lead to reduced rates of elective procedures. Springbuk can put the information you need at your fingertips to determine where to focus your efforts and track the impact of implemented strategies.


For more insight into Telehealth utilization trends, download the 2021 Employee Health Trends Report.

Or, read the posts in this series on trends:



References:

  1. https://www.dartmouthatlas.org/downloads/reports/Joint_Replacement_0410.pdf

  2. Lurie JD, Bell JE, Weinstein J. What rate of utilization is appropriate in musculoskeletal care?. Clin Orthop Relat Res. 2009;467(10):2506-2511. doi:10.1007/s11999-009-0889-4

  3. https://www.milliman.com/-/media/products/aco-insight/pdfs/preference-sensitive-procedures-preference-sensitive-conditions.ashx

  4. Boss EF, Mehta N, Nagarajan N, et al. Shared Decision Making and Choice for Elective Surgical Care: A Systematic Review. Otolaryngol Head Neck Surg. 2016;154(3):405-420. doi:10.1177/0194599815620558]

  5. https://www.ahrq.gov/sites/default/files/publications/files/optionsposter.pdf

MEET THE AUTHOR

Janet Young, MD

With more than 20 years of experience, Janet Young has provided clinical expertise and oversight to the development of healthcare analytics used in provider, payer, employer, and government sectors. Most recently, Young served as a lead clinical scientist at IBM Watson Health, guiding clinical content development related to new models, methods and analytics using claims, EMR, Health Risk Assessment, and socio-demographic data.