Lessons from Credit Bureaus for Improving the Market for Electronic Medical Records

Article excerpt

Electronic medical records (EMRs) have tremendous potential to lower consumer medical costs and improve quality, yet their use in the United States remains extremely low. We explain the reasons for this market failure and then document the strikingly parallel problems in the lending market one hundred years ago. We investigate how the evolution of credit bureaus helped address market limitations in lending, and ask whether similar entities--what we call health information bureaus--might overcome similar problems in EMR adoption and interconnection.

**********

There are numerous clinical, academic, research and administrative uses for a patient-based electronic medical record (EMR), which stores a person's complete medical history in a digitized form. Patients can take better charge of monitoring their health; doctors and hospitals can simultaneously view and share information and research labs have new sources of medical information. Hillestad et al. (2005) estimated that EMR implementation in the United States at a 90% adoption rate could eventually save over $77 billion annually in medical costs. Costs of adoption would average $6.5 billion per year for inpatient systems and $1.1 billion per year for outpatient systems (Hillestad et al. 2005), so the annual net savings to society could eventually reach over $68 billion a year. Despite these advantages, fewer than 5% of US physicians and hospitals use full-scale EMRs (Cutler, Feldman, and Horwitz 2005; DesRoches et al. 2008; Jha et al. 2006, 2009).

There are four significant reasons. First, one person's savings is another person's loss of income. Taylor et al. (2005) points out that EMR adoption would save an estimated 404,000 lives through improvements in disease management and prevention, but providers would bear "annual revenue decreases of $51.7 billion for hospitals, $11.6 billion for physician services, and $13.5 billion for pharmacies" (p. 1237). (1) Second, as a network good with high fixed costs, EMRs' unit value rises exponentially as the number of units or users in the market increases. Thus, there is little benefit to be an early adopter (Taylor et al. 2005). Third, when one provider shares electronic patient information with another health care provider, the record transforms from the doctor's private property to a quasi-public good. Ironically, doctors and hospitals do better financially if their information sources are more inefficient (Grossman, Kushner, and November 2008; Kleinke 2005). Fourth, providers adopting EMR technology capture only 3.1% of the total social benefits (calculated using data from Exhibit 2 in Hillestad et al. (2005). Private insurance companies, Medicare, Medicaid and eventually consumers of medical care are the primary beneficiaries, yet these groups do not directly pay for the maintenance of the EMRs (Hillestad et al. 2005).

Given these roadblocks to an efficient market outcome, the question is: Which combination of government regulation and private incentives would steer the market in the right direction? Rosenfeld, Zietler, and Ferguson (2004) suggested that the best way to solve this market failure is for public and private insurers, who are among the primary beneficiaries from EMR technology, to play a larger role in footing the bill. Insurers could pay for EMRs through financial incentives that reward or penalize providers and patients, based on their use of EMRs.

Even if insurers provided financial incentives, two inherent problems would remain. First, there are high transaction costs related to gathering large numbers of buyers and sellers of EMRs to the negotiating table to agree on a uniform standard. Financial support by insurers does not solve the problem of different insurers adopting incompatible systems, or their unwillingness to provide information to others when their subscribers switch to competing insurers. Second, health care providers have an array of preferences regarding EMRs, depending on factors such as the size of the practice and age of the physician (Centers for Disease Control and Prevention 2005). …

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.