Health information exchanges (HIEs) have been touted for their potential to reduce societal healthcare costs, but pricing and subscription policies for organizations participating in an HIE have been hard to compute. A team of researchers from the University of Wisconsin-Madison (UW), however, may have made the task easier thanks to a linear programming (LP) model.

“We proposed a framework, based on LP, that allows for more sophisticated analysis of the financial consequences of an HIE on each of its participating institutions,” wrote Srikrishna Sridhar, a graduate student at UW, and colleagues in a study published in the November issue of the Journal of the American Medical Informatics Association. “The specific HIE pricing recommendations in this report apply only to the study population, but our framework can be generalized to other settings.”

Sridhar and colleagues aimed to develop a model that would quantify the financial worth of HIE information to participating institutions and evaluate a trio of HIE pricing policies—fixed-rate annual subscription, charge per visit and charge per look-up.

The authors used information from the Wisconsin Health Information Exchange (WHIE) and applied the framework to 4,639 emergency department (ED) encounters over a 12-month period at the three original participating members of WHIE.

What they found was that HIE data access produced net financial gains for all providers and payers, though payers benefitted more. Providers with a higher number of health maintenance organization patients also saw more significant gains. More than 70 percent of the savings came from a reduction in unrequired hospitalizations and through avoiding repeat ED visits.

While the model showed more benefit to payers, attributable to decreases in reimbursement payments, Sridhar and colleagues said providers in the study population benefited thanks to a unique mix of patients. “Providers lost money on commercial [fee-for-service/FFS] patients due to a reduction in patient volume coupled with an increase in the expense-to-reimbursement ratio. However, this loss was offset by a benefit: reduced expenditure on a much larger group of Medicare/Medicaid (M/M) patients. Three specific characteristics of the study population stood out:

• There were four times as many M/M patients as commercial FFS.
• Institutional charges for M/M patients were 25 to 85 percent less than similarly diagnosed patients with commercial FFS insurance.
• Provider compensation was half as much for M/M patients as for patients in the commercial FFS population.

The LP model demonstrated that fixed annual subscriptions could sustain the HIE and ensure financial gains for all participating organizations. Sridhar and colleagues also said that even if a per-look-up charging scheme is in place, benefits for looking up medical information for every ED patient outweigh the costs.

“Our specific HIE pricing recommendations depend on the unique characteristics of this study population,” wrote the authors. “However, our main contribution is the modeling approach, which is broadly applicable to other populations.”

They explained that a financial model for HIE must take into account uncoordinated actions of payers, providers and the HIE organization itself--actions that may conflict with optimal system-wide performance. The HIE must include enough trustable data to instantiate the model and precise terms must be formulated for key objectives, constraints and decisions.

“Our approach may have the greatest value for agents or healthcare delivery systems seeking sustainable pricing and subscription schemes for HIE use,” wrote the authors. “Our model suggested that a flat annual fee for all agents is preferable. However, more data should be incorporated in the model before guidance on fee structures can be provided to support other environments.”