PointRight’s increasingly sophisticated use of analytics in the medical arena ensures that care establishments are protected in any emergency.
Insurance claims resulting from a charge of medical malpractice are all too common in the US, forcing some doctors to think twice about the level of service they offer patients, who ultimately lose out the most.
But the medical profession—doctors, nurses, hospitals and, to an extent, patients—is fighting back through its support of medical liability reform, which has now been boosted by the provisions contained in President Obama’s recently passed healthcare reform bill.
Detailed analysis is needed, however, and PointRight, the industry leader in providing data-driven analytics, is leading the way. US Captive spoke to executive vice president of insurance Mary Chmielowiec and managing director Barry Fogel to understand the methodology behind the analysis of liability, and the benefits it provides to the medical industry.
Why did PointRight get started in analysing liability?
We have been providing consulting and data analysis to nursing homes for 15 years, but in 2004, we began to really focus on helping nursing homes reduce their professional liability exposure by improving their quality and identifying high-risk situations.
When we tried to sell these services, we had the naive idea that people should get discounts from commercial carriers, but we soon realised that there’s no such thing as a regular price; so we began to assemble a database of nursing homes’ professional liability loss histories so that we could do predictive modelling on who gets sued and why. So far, we have collected loss data on more than half of the nursing homes in the US.
What sort of information and tools are available to measure risk?
We have come up with a way to measure and stratify risk. The quantitative analysis allows us to identify which risk management programmes will have the most impact on risk reduction. We use very technical predictive risk models that by analysing, managing and controlling the losses can enable captives to act more intelligently and less intrusively.
For example, our underwriting analytics make use of a comprehensive database of public and private information such as occupancy, payer mix, staffing analysis, survey results, survey deficiencies, and measures of structure, care processes and clinical outcomes. These are combined with data elements of known predictive value, which are systematically extracted from the application and from the loss history.
Through our diagnostic process, a facility’s performance is benchmarked against that of other facilities within its state, survey district and rating territory. Potentially modifiable risk factors that deviate adversely from the benchmarks and have significant impact on risk are identified, and a risk management prescription is developed that focuses on those factors.
"By analysing the loss itself, we can lower the cost of the claim. For example, the combination of quick action and good analystics has taken a case that could have resulted in a $75,000 lawsuit and settled it for $10,000."
For risk management, a facility may make use of its Minimum Data Set (MDS). The MDS is part of the US federally mandated process for clinical assessment of all residents in Medicare or Medicaid-certified nursing homes. This process provides a comprehensive assessment of each resident’s functional capabilities and helps nursing home staff identify health problems. The completion of an MDS record at least four times a year is a requirement for facilities receiving any government money. For a facility, our tool analyses an MDS record and identifies risk in the residents. The tool is focused on high-risk monitors and identifies residents who are likely to have a fall, who suffer with pressure ulcers or who are near the end of life.
If we are looking at a particular member of a captive and it seems to have a problem with residents with pressure ulcers, a tool like this could be put into its system so that it can monitor these residents and hopefully avoid anything that might lead to litigation.
How do you help captives manage claims?
Each captive is different when it comes to managing claims. Some do it themselves and some use a third-party administrator (TPA). It is very typical for a TPA or claims manager to have a one-size-fitsall attitude. Some captives will fight everything, some will bury their heads in the sand and some will pick the lawyer with the lowest hourly rate. Using analytics, you can efficiently triage the claims and decide whether each claim needs to be settled promptly (as it may have a huge exposure) or be defended (as the facility may be in the right). A TPA or a captive can be much more specific about which lawyer it hires, and this could ultimately be the difference between winning and losing. It certainly will impact the settlement amount.
When a claim occurs, how can a facility determine its defensibility?
If there is a claim, tools—via the use of models—can analyse in a couple of hours how bad the claim is for the facility. We can determine whether it is going to have trouble defending itself (because of its track record), or whether this is unusual for the facility and it would have a very good case in defending the claim.
How can you help a facility when proving the cause of the claim?
Getting to the heart of the claim and figuring out how much it is going to be, or whether it should be settled, is a key point. By using data analysis to take some of the emotion out of the case and by presenting the raw facts, you may be able to settle. The family may be very upset; however, the situation may have been inevitable based on the health condition of the patient. We can pull together an analysis, which looks at a particular resident and compares them to the rest of our database, to see what the likely outcome is— for example, if a patient died suddenly because of bad care or if they died due to the effects of illness.
How can loss analytics lower settlements?
By analysing the loss itself, we can lower the cost of the claim. For example, the combination of quick action and good analytics has taken a case that could have resulted in a $75,000 lawsuit and settled it for $10,000. In this case, a facility was being sued. It was alleged that the care planning was inadequate and that there was no fall prevention. However, deeper drilling into the MDS record for the resident uncovered that the resident’s overall health and low propensity to fall compared to a national benchmark backed up the facility’s decision not to care plan or restrain this resident, because the likelihood of a fall was low. By spending a little money up front and putting facts in front of an attorney, you can save a lot of money in the long run.
How can captives monitor risk management performance?
Some captives try to select who they will and won’t allow in, and some let anyone and everyone in. Some have differential pricing and others don’t. Whatever their philosophy, when you know what the risk is, you can work towards making sure it doesn’t happen.
Obviously, if they exclude the worst risks, they’re going to be better off, but if everybody’s in, you can still determine who has specific risk management requirements and needs money spent on improving this. For example, a facility with poor staffing could be told that if it wants to be in a specific captive then it needs to improve that area.
How can companies detect a ‘lucky’ versus ‘unlucky’ risk?
Lucky is someone who is not a very good performer but hasn’t gotten hit yet, and unlucky is someone who is performing pretty well but still something unexpected goes wrong.
If you can measure where a facility has risk, then you can determine whether a claim was just bad luck or if this could have been prevented. If there is an area in which the facility is lacking, the captive can put pressure on the facility to make changes. Without quantitative tools, it becomes one person’s opinion against another’s.
With this in mind, you can run a portfolio with a much greater degree of predictability of when you should expect claims and when you will need this cash.
What captives need to get a better handle on are the folks who have been lucky, because their luck is eventually going to run out and, if they are not prepared, this could result in a disaster.
How can you help facilities to invest in the right places?
People might think they know how to tell a good nursing home from a bad one, but this is just based on what they see on the surface. Our models allow a much more in-depth view. The models look at the quantity of risk, where facilities will get the greatest monetary impact and how they can judge mediocre nursing homes that are neither good nor bad, but can be higher or lower risk as a result of a mismatch of who they’re treating and how. The quality of the nursing home will present different concerns. If you have a pressure ulcer claim in a mediocre facility that was understaffed, that is going to be a big exposure. By looking at the characteristics of the facility, we can tell which ones are good and which are bad.
How can following the claim through every stage, from underwriting through to claims handling and back, make a difference?
Measurements and rewards for captives are very important. If you understand where a facility is exposed in the underwriting process, you can give it its leverage points in the risk management process and protect it when losses come back around. By feeding it all back through when you underwrite again, you can handle and manage the captive much better.
Seeing the whole picture can provide a valuable insight as to where the captive should target its dollars. If a facility has a problem with complaints, then the captive needs to work on its communication strategy.
The thing that directs the attention of the facility is when the claim becomes a teachable moment. In the commercial insurance industry, you have a real isolation of risk and claims management, and when you have a captive, it is so much easier because it’s a smaller organisation and everyone’s a stakeholder, so you can knock out those boundaries.
Mary Chmielowiec is the executive vice president of insurance at PointRight. She can be contacted at: email@example.com
Barry Fogel is founder and managing director of PointRight. He can be contacted at: firstname.lastname@example.org