Paul Cohen

Obligatory disclaimer: All views are mine and do not necessarily represent the views of my current or former employers. This post will also be very boring if you don’t care about employee health benefits.

 

Over the past few years, I’ve chatted with many healthcare startups looking to break into the employee benefits space. The question I get most often is “What do employers know about the health needs of their employees?” There are several ways to answer the question, but I’m going to focus this post on explaining the types of data that small to midsize employers (500-5,000 covered lives) receive from payers.

 

For context, I’m not an employee benefits expert (nor do I aspire to be one), but I’ve spent a lot of time leading value-based care programs with employers, brokers, consultants, and payers. As a result, I’ve been intimately exposed to the nitty-gritty details of how employers of all sizes — from 500 to over 50,000 lives — make benefit decisions.

 

 

The Challenge & Opportunity of Employee Benefits

HR leaders have a hard job. To gain empathy for the problem, imagine you lead HR at a technology or professional services firm that employees about 1,000 people. If the average age is in the mid 30s, you’re probably funding care for about 1,500 members (including spouses, children, and other dependents). Assuming each member incurs about $5,000 in total medical, pharmacy, and administrative costs per year, you are in charge of about $7M to $8M in annual spending.

 

Given this level of responsibility, HR leaders need insight into their population. Most benefits managers are not clinicians or data scientists, so they need help to make the right decisions for their employees. Many large self-insured organizations pay consultants to use claims data to generate insights and provide recommendations. However, small and midsize employers primarily make decisions based on data provided by the insurer. These employers are generally less empowered with the right information and insights, and it’s not their fault.

 

 

What Payers Provide

The data that payers give employers varies based on several factors. The biggest determinant is whether the employer is self-insured or fully-insured. Self-insured employers generally have at least 1,000 covered lives. At this scale, they bear risk for the medical costs incurred by their population. They use the payer for administrative services only (ASO). The payer provides the network, pays claims, manages enrollment, conducts utilization management, and delivers a variety of other administrative services. Self-insured employers often have more data because they are at risk for medical costs and essentially operate as their own health plan. On the other hand, fully-insured employers pay pre-determined premiums to the payer, and the payer bears risk. As a result, fully-insured employers generally have less access to data. Various privacy laws, which differ by state and city, also govern what data are available to employers.

 

These complexities aside, most employers have quarterly or biannual meetings to review utilization data. Payers have tools that generate standard Excel reports from claims data. Account management teams typically build PowerPoint slides that use these templated reports. Employers, payers, and often the employer’s benefits broker (unless they use a PEO) will sit down for an hour or two to discuss utilization data and review opportunities for improvement. Depending on the size of the employer and the importance of the relationship, payers will bring a several account management representatives, a doctor or nurse, and sometimes a pharmacist.

 

While every payer is different, the reporting packages are fairly similar in the small to midsize market. I’ll break down the key components and give you a sense of what’s generally included and what’s not.

 

 

1) Summary Demographics, Utilization, and Cost

The first section typically provides the employer with basic demographic data and key utilization metrics. All reporting usually includes current period data, prior period data, and “book of business” benchmarks (i.e., comparisons to the payer’s other employer populations).

 

Typically Included

Typically Not Included

Demographics & Enrollment Number of employees and members Average age Gender mix   Plan Selection (sometimes) Enrollment by HDHP vs. PPO vs. HMO   Cost & Utilization Total spend on medical and pharmacy claims Total spend per employee, per member; year-over-year trend comparisons Spending per employee for some categories (inpatient, ambulatory) Cost burden distribution (employee vs. employer) Basic utilization statistics (denominated per thousand employees): admissions, inpatient days, average length of stay (ALOS), ER, office visits

Costs by plan type Demographics by plan selection Pharmacy metrics or trends (in a separate section) Other important utilization metrics (e.g., OON spending, urgent care utilization, PCP visit rates) Use of other services, such as video care, chronic disease programs, care navigation

 

People assume the summary contains the most important metrics. However, the summary is merely a read-out of whatever data are in the default reporting package. These topics might not be relevant for an employer and are generally provided with no sense of scale or importance. Additionally, there are no indications of what differences are meaningful. Distracted by the deluge of metrics, people may waste time trying to interpret data that likely has no significance (e.g., a 5% change in ER visit rates on a base of 1,500 lives).

 

 

2) Utilization & Cost Breakdown

Payers generally provide a breakdown of spending by category, with charts that show the percent of total spend by category and year-over-year cost trend comparisons.

 

Typically Included	Typically Not Included
Total spend by category (inpatient, ER, office visits, radiology, professional services, lab); trend for each Unit cost and utilization rates for some of those categories	Unit cost vs. volume drivers for each category (i.e., to understand what contributed to changes in costs) Definitions for jargony categories (e.g., “professional services”)

 

To make matters more complicated, the medical services categories that most payers use are highly confusing. Having gone through the exercise in the past, I’ll admit that categorizing each procedure, service, and item delivered in healthcare is challenging. However, the reports that employers receive are generally incomprehensible and incomplete, and often lead to incorrect conclusions.

 

Here’s an example of how a payer might categorize healthcare spending (all data is fictional but directionally accurate):

 

	% of Total Paid Amount	% Year over Year Trend
Inpatient facility	25%	-2%
Office visits	15%	-5%
Professional services	15%	-9%
Ambulatory facility	10%	5%
Mental health	10%	+40%
Medical pharmacy	10%	-20%
Emergency room	5%	+30%
Radiology	5%	+12%
Labs	5%	-10%

 

This data is confusing because some of the categories are places of service — inpatient facility, ambulatory facility, emergency room — while others are types of services — lab, radiology, home health. Furthermore, there is no data on unit cost and volume, so there is no way to dissect the drivers behind changes in medical trend. It is also difficult to evaluate the success of interventions (e.g., an ER avoidance campaign) when the relevant data is not reported.

 

While it is impossible to draw conclusions from this data, smart people can tell any story. You can imagine the confusing conversation that results:

• Are procedures shifting from inpatient settings to ambulatory surgical centers? Or is the total volume going down?
• Maybe the volume of both are increasing, but costs are lower because the payer negotiated better facility rates?
• Or perhaps inpatient admissions are increasing, but total costs are lower because fewer people had babies?
• Wouldn’t fewer babies mean lower radiology costs? But are sonograms done by a hospital-based obstetrician listed in the radiology or inpatient facility category?

 
 

3) Network Utilization

Typically Included	Typically Not Included
% of spending at in network providers Use of preferred or narrow network providers Cost savings from discounts negotiated by the carrier Breakdown of out of network spending in several categories (e.g., inpatient, office visits)	Detailed breakdown of out of network utilization by category (e.g., psychotherapy visits, physical therapy visits) Top out of network providers Top procedures or services with out of network utilization

 

The big challenge with this section is that it is difficult for employers to understand trends in OON utilization. I often see OON spending cluster in a few areas, such as physical therapy and psychotherapy, but these patterns are invisible in most payer reporting. As a result of these gaps in reporting, employers struggle to take action to improve in-network care for their employees.

 

 

4) High Cost Claimants

High cost claimant reporting isolates the effects of high cost members (typically $50K+ or $75K+ in total TTM spending) from the rest of the population.

 

Typically Included	Typically Not Included
Total cost of care for non-high cost claimants vs. high cost claimants Prevalence of high cost claimants in population Top diagnoses for high cost claimants, broken out by spending categories (e.g., ambulatory, inpatient)	Utilization rates (e.g., inpatient admits) for non-high cost claimants vs. high cost claimants Pharmacy components of high cost claimants

 

This section is generally useful, though this is usually the only area where the distortionary effects of catastrophic events are removed from utilization reporting. As a result, employers can make incorrect conclusions because the high cost claimant exclusions do not propogate throughout the reports. In addition, even when employers use a payer’s PBM, the reports never link high cost claimants across medical and pharmacy spend. Unfortunately, payer reporting reflects the structure of the data instead of the structure of healthcare. The decoupling of medical and pharmacy costs understates the impact of high cost claimants. This is an obvious but pervasive oversight.

 

 

5) Inpatient & ER

Payers generally provide several breakouts on inpatient and ER utilization.

 

Typically Included	Typically Not Included
Key metrics: inpatient admissions, inpatient days, average length of stay (ALOS), ER visits Most frequently used hospitals and total dollars spent at each High level diagnostic categories for inpatient admissions	Breakdown of admissions as planned or unplanned Breakdown of admissions by category (e.g., maternity, surgery, trauma, etc.) Inpatient readmission rates ER avoidability ER visits by type of member (e.g., employee, child dependent, adult dependent) Urgent care and convenience care utilization

 

Gaps in inpatient and ER reporting present significant impediments to insight. The key issues I have seen are as follows:

1. It is difficult to understand inpatient admission trends because full-term deliveries, which represent about 40%-50% of admissions in younger commercial populations, are typically not broken out from other inpatient admissions. It is hard to know if changes in inpatient admission rates are good or bad if a significant portion is driven by a positive but costly event.
2. ER visits are usually reported without context. The highest volume ER visit reasons — chest pain, headache, syncope and collapse, unspecified abdominal pain — can be difficult for even experienced clinicians to evaluate on paper. Encouraging HR leaders to postulate about whether abdominal pain was avoidable is not a good use of time. Fortunately, there are several algorithms that provide estimates of ER visit avoidability (e.g., the validated NYU ED algorithm), which could help employers understand opportunities for improvement. However, I seldom see an avoidable ER visit metric in a standard payer report.
3. Similarly, each payer categorizes ER and inpatient visits using their own medical diagnosis groupers. Categorizing diagnoses is difficult, but the categories that payers use can be incredibly confusing. What does an ER visit category called “Blood/Organs” mean?
4. ER visits are not broken out by member type, which is particularly relevant for populations with young children. Pediatric ER visits can be a significant driver of utilization, and many interventions to reduce adult ER visit rates do not apply to children.
5. Despite the rise in urgent care and convenience care (i.e., walk-in clinics), metrics on these types of care are not commonly included in major carrier reporting. The lack of data can lead to misplaced initiatives. For example, many employers have been trying to reduce ER visits by encouraging the use of urgent care. This strategy can work, but promoting urgent care creates new demand for medical services. I have seen several cases in which the cost savings of fewer ER visits was offset by disproportionate increases in expensive urgent care visits. Substitution effects are sometimes mitigated by induced demand. Employers need data on these dynamics to more accurately evaluate performance.

 

 

6) Pharmacy

Pharmacy reporting is similar to the medical reporting in that it decomposes spend by demographic factors and clinical categories.

 

Typically Included	Typically Not Included
Total pharmacy costs per member Cost burden: patient vs. employer Number of claims; claims per person Average copay Brand vs. generic utilization	Top medications dispensed Detailed breakouts on specialty pharmacy costs Drug categories that reflect how medications are used Variation in the cost of infused medications

 

Pharmacy spending is not immune from ontological challenges. Payers usually cluster medications into proprietary categories that are informed by GPI drug groups. These ontologies do not reflect therapeutic uses in ways that employers can understand; e.g.,:

• Anti-Infective
• Biologics
• Respiratory
• Central Nervous System
• Topical

 

It is difficult to know what to do with these labels. Even the deceptively straightforward “anti-infective” category includes antibiotics used to treat traveler’s diarrhea, antifungals for toenail infections, and antiretrovirals to treat HIV. It is hard to create medication ontologies that fully reflect therapeutic use (e.g., “diabetes medications”) because a single medication might be used to treat different conditions, but we can definitely do better than the current approaches.

 

 

7) Quality

Quality measurement is not a significant component of standard payer reporting. Employers will typically receive a slide that shows the percentage of employees who have used the most common preventive services. This format is extremely dangerous because it implies that very few employees are receiving appropriate preventive care. However, coverage levels and care needs are not the same thing. For example, a plan might cover yearly pap smears, but most women do not need (and should not get) yearly pap smears. Utilization reporting misrepresented as preventive care metrics leads HR leaders to perceive that their employees need more preventive services. Payers have spent billions of dollars incentivizing primary care providers to improve industry standard HEDIS quality measures (cancer screenings, vaccinations, and chronic care management), but I rarely see them in employer reporting.
 
 

Conclusions

Employers get a bunch of data, but almost no insight. Companies selling to small and midsize employers need to understand these limitations. We only know about the pain points we feel, and the unfortunate reality is that many employers are largely unaware of their opportunities for improvement. In addition, vendors need to help employers understand the value of their new employee benefit post-implementation. Many employers simply do not have the tools to measure a vendor’s impact on healthcare utilization. Finally, these gaps suggest there is a potential market for companies that leverage claims data to provide small and midsize employers with actionable insight.
 
HR leaders want to do right by their employees. It is up to the rest of the industry to help them do that.