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Posted on Oct 29th, 2020

Attacking Expert Opinions based on Big Data

By Jason G. Schutte

 Synopsis

There is an ever-growing industry of experts offering to provide services in personal injury and casualty litigation based on evaluation of property damages and medical charges taken from analysis programs and/or statistical data. These opinions are often derived from big data gathered from various insurance companies. This fact creates major questions and problems for litigants and attorneys in establishing an evidentiary foundation to admit these opinions into evidence.

Underlying facts of case:

Verci v. High is a personal injury case wherein plaintiff asserted that she was injured as a result of the negligence of the defendants. The majority of plaintiff’s medical charges arose from her treatment with Dr. Kube and related entities. The major issue of contention in the case was the reasonable value of the medical services provided by Dr. Kube.[i]

Plaintiff claimed over $1 million in charges for her medical care and treatment. Approximately $800,000.00 arose from her treatment with Dr. Kube and related medical entities.[ii]

The Tazewell County, Illinois trial court entered an Order prohibiting the defendants from cross-examining Dr. Kube and associated medical entities about the cash value advertised for the medical care they provided. The trial court allowed the defendants’ billing expert to testify regarding her opinion about the reasonable care of the medical services provided by Kube.[iii]

Defendants presented an expert to testify that the usual, customary and reasonable total for the charges incurred were approximately $148,118.00. The expert determined that the charges submitted by Dr. Kube were 547% higher than cash prices that Kube’s entities had advertised on line.[iv]

Defendants’ expert testified that she relied upon three databases in determining the reasonable value of the medical services provided, including the (1) FAIR health, (2) Optum, which utilizes FAIR health data and (3) American Health Directory. FAIR was the primary database that she used.[v]  

Defendants’ expert did not make any effort to obtain information from individual medical providers or outpatient centers to determine the value of their charges.[vi]

FAIR Health obtains all of its data from charges received by insurance companies that have been charged by medical providers. FAIR then categorizes the charges geographically and creates a range of the charges.[vii]

Defendant’s expert assumed that Optum and FAIR correctly performed their data/statistical analysis. She did not review the raw data that Optum or FAIR obtained. She did review their statistical analysis.[viii]

The certified questions that the court issued were:

(1)“Did the trial court err in denying defendants’ right to cross-examine Dr. Kube and his associated medical entities with prices advertised by Dr. Kube and the same associated medical entities as prices that represent the reasonable value for the services rendered[?]”

(2)“Did the trial court err in allowing defendants’ billing expert, Rebecca Reier, to testify over plaintiff’s objection when the defendants’ expert relies upon geographically zipcoded information collected by national databases rather than personally obtained medical billing comparisons[?]”[ix]

Appellate Court Ruling

Admissibility of Advertised Cash Prices:

The Appellate Court determined that the trial court erred in prohibiting the defendants from cross-examining plaintiff’s treating physician, Dr. Kube, about the cash prices that his medical entities advertised for their services. The court found that the range of fees could be charged for services plaintiff received was admissible and not barred under the collateral source rule. Hence, the defendants should have been allowed to cross-examine Kube regarding the advertised cash prices.[x]  

Admissibility of Reier’s Testimony:

The Appellate court noted that defendants’ expert relied primarily on the FAIR health database. They also noted that the information contained therein is no evidence of what other area providers charge for the services plaintiff received because the data therein came from unknown numbers of insurance companies rather than healthcare providers. Additionally, the databases were used to determine reimbursement rates rather than the reasonableness of provider charges. Additionally, the data in the database was incomplete.

The court further emphasized that the FAIR database does not include information for amounts charged to uninsured individuals hence it was not a true representation of what medical providers charge. Likewise, defendants’ expert could not identify any medical providers whose charges were included in the FAIR health database nor could she state whether any specific provider’s charges were included in the database. The Appellate court emphasized that expert witnesses are not allowed to testify that providers’ medical charges are unreasonable based upon reimbursement rates as those are irrelevant and violate the collateral source rule.[xi]  

Practical Effect of Case

The major problems with the opinions from the expert in this case were that they were based on unverifiable information that did not come from the entities that would actually provide the service being examined. Also, the data examined (reimbursement rate) could not be used to determine a reasonable charge for the treatment. Likewise, the data did not include all types of charges, such as charges to uninsured patients.

Whereas, the evidence of the cash charges from Dr. Kube was admissible because it was verifiable and from a geographic source that provides the type of care at issue. The key to admissibility of such an opinion is whether the data from which the opinion is based can be verified. Is it from a source that actually provides the services being evaluated (for instance, a carpenter vs a warranty company)? Does it include all charges available for that service (for instance reduced rates for members versus nonmembers)?

I have seen expert witnesses presenting opinions based on big data and information taken from various data bases and computer programs in many different scenarios. These have included lost earning capacity, cost of repairing property damaged by water/fire, value of personal property damaged by fire and future medical specials, to name a few.

I have always believed that it is truly difficult to establish a proper foundation to admit opinions based on this big data into evidence at trial. I believe this case provides an excellent road map to contest opinions that are based on this such data. Of course, the trial judge will have wide discretion regarding the admission of such opinions. At a minimum, this case provides a good basis to attack any such opinion during the discovery and trial phase of litigation.

Attorneys and claims professionals presenting opinions based on this type of data should carefully scrutinize their expert witness and whether they can provide the basic testimony to convince the trial court that the underlying information is sufficiently reliable to admit the opinion into evidence. If it is not, then you need to build up the opinion/ witness or, get a new witness.

Attorneys and claims professionals opposing such opinions should closely review any and all computer programs, data sources and investigation performed by the expert to find any basis to criticize and discredit the opinions as based on unverifiable or inapplicable data. If justified, you may be able to exclude or limit such testimony via a Motion in Limine.

 

[i] Verci v. High et.al., 2019 IL App (3d) 190106-B;

[ii] Id. at par. 4;

[iii] Id. at par. 2;

[iv] Id. at par. 5;

[v] Id. at par. 6;

[vi] Id. at par. 10;

[vii] Id. at par. 7;

[viii] Id. at par. 9;

[ix] Id. at par. 13;

[x] Id. at par. 25;

[xi] Id. at par. 32;

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