Session Title Competition in Highly Regulated Sectors: Telecom, Health, and Financial Services Session Chair Sharon Oster (Yale University) Manuscript title Information Overload and Information Technology in Health Care Authors James B. Rebitzer, Case Western Reserve University and National Bureau of Economic Research. Contact information: Case Western Reserve University, 11119 Bellflower Road, Cleveland, Ohio 44106 USA, Phone: (216) 368-4110, Fax: (216) 368-5039, E-mail: james.rebitzer@case.edu. Mari Rege, Case Western Reserve University. Contact information: Case Western Reserve University, 11119 Bellflower Road, Cleveland, Ohio 44106 USA, Phone: (216) 368-4185, Fax: (216) 368-5039, E-mail: mari.rege@case.edu. Christopher Shepard, MD, University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, Phone: (216) 707-9539.
Information Overload and Information Technology in Health Care James B. Rebitzer, Mari Rege and Chris Shephard Economists who study incentives in organizations have given much attention to problems that arise when information is scarce. Indeed the premise of the vast literature on optimal incentives is that principals have incomplete knowledge about agents and their actions. It has long been recognized, however, that other important features of organizations are the result of a superabundance of information (Simon 1970), but economic research on these aspects of organizations is less well developed. In this paper we consider economic issues that arise when agents are presented with more information than they can handle. Our purpose is to assess ways in which new information technology (IT) can mitigate problems that result from this information overload. The focus of our study is information overload among physicians. In medicine, the number and variety of diseases and treatments threaten to overwhelm the information processing capacities of individual doctors. This complexity is magnified by the rapid growth of new, medically- relevant knowledge. Failure to cope with this flood of information can cause physicians to overlook important new treatments or clinical trails altering the recommended use of existing treatments. The net result is a degradation of care quality and a slower diffusion of new medical innovations (Institute of Medicine 2000 and 2001) In this paper we investigate how IT can help physicians cope with information overload and therefore improve care quality and the rate of diffusion of new innovations. Our analysis focuses on a particular type of computer system that has drawn considerable attention in the medical community and in public policy debates: a computer based decision support tool that 1
scans digitized patient records and recommends treatment protocols to physicians when it finds a deviation from best practice. The paper proceeds as follows. In section one, we briefly discuss evidence suggesting a slow and uneven diffusion of new medical knowledge. Slow diffusion is likely the result of physicians being unable to keep up with newly emerging protocols. Thus in section two we outline a simple conceptual framework for thinking about physician learning in an environment with too much new information. In section three we present evidence from a randomized prospective trial suggesting that computer based decision support tools substantially enhance the diffusion of new medical knowledge among physicians. From a public policy perspective this is an important finding. Reducing the lag between the discovery and application of new clinical developments is likely to improve quality of care and increase the returns to innovation. We conclude by considering whether private incentives to invest in computer based decision support technology are sufficient to assure adequate investment. I. The Slow and Uneven Diffusion of Innovations in Medicine The broadest evidence suggesting a slow diffusion of innovations comes from the vast medical literature on geographic variation in practice patterns. The care delivered by doctors varies greatly from one geographic region to another in ways that cannot be accounted for by underlying differences in patient population, medical prices, technology or other exigencies (Phelps 2000, Skinner, Fisher and Wennberg 2001). In the absence of other explanations, it is hard to escape the implication that these variations are due to the incomplete diffusion of the best medical practices to physicians. Consider, for example, the case of heart attacks. Some states spend 40-60% more per patient than others with no improvement in outcomes. This 2
suggests substantial productivity differences across regions that are partly the result of the uneven diffusion of new innovations (Skinner & Staiger 2005, p. 20). The substantial resources that drug companies spend promoting their products also suggest an imperfect diffusion of medical knowledge. If the results of clinical studies percolated swiftly to providers, pharmaceutical companies high-powered marketing campaigns and sales forces would not be effective. The literature suggests, however, that physicians are affected by marketing. Avorn (2004) documents a number of instances where pharmaceutical marketing appeared to successfully promote the use of favored drugs even when there was good clinical evidence that these drugs were ineffective or less effective than inexpensive alternatives. A recent econometric study of the diffusion of anti-ulcer drugs by Berndt et al. (2003, p. 262) finds that equilibrium market shares are strongly determined by the resources drug companies devote to advertising. II. Conceptual Framework for Physician Learning Physicians are highly trained and highly motivated professionals. It is reasonable to assume, therefore, that the slow and uneven distribution of new and effective therapies is not the result of simple inattention. A more likely explanation is that it is difficult for physicians to keep up with the rapidly changing state of medical knowledge and to understand what these changes mean for the treatment of specific patients. In this section we outline a simple model for understanding how physicians learn about medical innovations relevant to their patients 1. Our starting point is that in the acquisition of new medical knowledge, physicians are hampered by two cognitive limitations: 1 For details on the formal model see Rebitzer, Rege and Shepard (2005). 3
1) The flow of new medical knowledge exceeds the information processing capacities of individual physicians. 2) It is difficult for physicians to link the description of new medical knowledge to the conditions of specific patients. Faced with these cognitive limitations, physicians will rely on the recommendations of their colleagues to economize on the costs of information acquisition. Colleagues are good at suggesting highly relevant treatments, but due to limits on their own cognitive abilities they are generally not very effective at keeping abreast of the newest procedures. Thus physicians will also devote time to independent reading in medical journals. Reading journal articles may expose the physician to the newest innovations, but they may fail to identify the patients for which these treatments are relevant. Rational physicians will therefore rely on both their colleagues recommendations and independent reading in new medical journals. Into this web of influence we now introduce IT enabled decision support systems. In these systems an artificial intelligence program compares a patient s treatment with best practice protocols drawn from the medical literature. If a discrepancy between actual and recommended care is observed, a message is sent to the physician. This message will link a specific patient with a specific medical literature, allowing the physician to consider the relevance of the literature to the patient s particular circumstances. In contrast to traditional learning modalities (colleagues recommendations and independent reading of medical journals) the computer based decision support tools are capable of suggesting treatments that are both new and relevant to the care of a specific patient. Heightened timeliness and specificity enhances physician learning and also displace the traditional avenues of information acquisition. III. The Effectiveness of Information Technology: Evidence from a Randomized Trial In this section we present new evidence from a randomized trial indicating that IT based decision support tools enhance the rate of diffusion of new medical knowledge. The data we use 4
comes from a randomized prospective trial of a decision support technology in which HMO members under age 65 were randomly assigned to study/control groups 2. Data from insurance billing records, laboratory feeds and pharmacies were then combined into a virtual electronic medical record and the information in these records was passed through a sophisticated program that scanned for deviations from evidence based, best-practice protocols. For patients in the study group, the information was scanned in real time and, if an issue was detected, a message was sent to the physician. The message stated the name of the patient, described the potential issue, and referenced the relevant medical literature. After the trial was completed, the control group data was analyzed and messages were generated that would have been sent to physicians if the control patient had been in the study group. The trial therefore allowed us to compare the rate of resolution of issues when physicians enjoyed decision support with the rate of resolution of the same issues when no support was available. We would like to know if the messages from the decision support tool increased the rate of adoption of new medical evidence. For this purpose we chose to look at the class of medications known as ACE-inhibitors. ACE-inhibitors were first approved by the FDA in 1981 for treatment of severe hypertension. Shortly before the randomized trial began in 2001, several major clinical trials, the most important of them referred to as the HOPE trial, established the beneficial effect of ACE-inhibitors in patients with mild and moderate hypertension, heart failure, past heart attacks, chronic renal disease, certain subgroups of diabetics and patients at high risk for cardiovascular events. Taken together, these trials greatly expanded the number of patients for whom an ACE-inhibitor was indicated. 2 For detailed descriptions of this trial see Javitt, Reisman et. al. (2005); and Javitt, Rebitzer and Reisman (2004). 5
In column one of Table 1 we analyze the eventual use of ACE-inhibitors for patients who didn t use these medicines but should have been using them on the basis of the HOPE trial findings. The dependent variable is a dummy variable equal to 1 if ACE-inhibitors were used during the course of the study while the dummy variable Study identifies patients assigned to the study group. Patients in the study group, whose physicians received computer generated messages regarding ACE-inhibitors, were thirteen percentage points more likely to start using the medicine than those in the control group: representing an increase of almost 100 percent. Comparing columns 1 and 2 it is clear that the decision support system had a more substantial effect on the relatively new ACE-inhibitor recommendation than on all other recommendations that suggested adding a drug. At the time of our study, the new clinical evidence regarding Ace inhibitors were widely promoted to physicians via the conventional routes of disease management programs and journal articles. We suspect on the basis of informal conversations that the computer generated messages had extra influence because they were reliable, timely, and focused physician attention on a specific issue concerning a specific patient. IV. Conclusions: Public Policy and Private Incentives Information overload acts as a drag on the diffusion of new knowledge and hence on the dynamic efficiency of the health care system. Using IT to alleviate this drag improves care quality and increases the return to innovation. This last outcome is desirable because it can stimulate more rapid innovation. Just as small increases in productivity growth rates accumulate over time to transform living standards; so enhanced innovation rates resulting from the faster diffusion of new knowledge should yield transformations in the quality of health care Most of the expense of IT based decision support systems are currently borne by care providers and/or insurance companies. Our model of physician learning suggests, however, that 6
neither of these parties have incentives adequate to support optimal levels of IT investments. The problem results directly from the way physicians learn about new innovations. If a doctor hears about ACE-inhibitors from the IT system, she will likely transmit that information to other physicians who do not have the benefit of computerized decision support. These spillovers improve care quality, but the benefits do not accrue to the physicians and insurers who pay for the system. The conventional economic response to positive externalities is to internalize them with subsidies financed by lump sum taxes. This approach is, however, famously difficult to implement. An alternative approach might be feasible because what appear as externalities to health care providers and insurers are actually sources of revenue to other market actors. The pharmaceutical and device manufacturers profit from the informal spread of new information about their products and therefore have more powerful incentives than providers and insurers to promote the spread of computer based decision support tools. Our model suggests that this finance scheme is also likely to yield unsatisfactory outcomes. This is because the limitations on physician cognitive capacities that lead to information overload also make physicians receptive to marketing messages sent via computer based decision support systems. Using the IT system to market products rather than to educate physicians about new protocols undermines the technology s effectiveness and also crowds out other, higher quality, information acquisition activities. We are left to conclude that appropriate investments in IT based decision support tools can enhance the quality of health care and the dynamic efficiency of the health care system. Designing public policies that assure appropriate investments in IT based decision support 7
systems in the health care setting is a difficult and important area for future research (President s Information Technology Advisory Committee, 2004). 8
References Avorn, J. Powerful Medicines. The Benefits, Risks, and Costs of Prescription Drugs. New York: Knopf, 2004. Berndt, E. R., R. S. Pindyck and P. Azoulay. Consumption Externalities and Diffusion in Pharmaceutical Markets: Antiulcer Drugs. Journal of Industrial Economics, 2003, 51(2), pp. 243-270. Institute of Medicine Committee on Quality of Health Care in American. To Err is Human: Building a Safer Health System (L. T. Kohn, J. M. Corrigan and M. S.Donaldson, eds.). Washington DC: National Academy Press, 2000. Institute of Medicine Committee on Quality of Health Care in America. Crossing the Quality Chasm: A. Washington D.C,: National Academy Press, 2001. Javitt, J., G. Steinberg, J. Couch, T. Locke, J. Jacques, I. Juster and L. Reisman "Use of a Claims Data-Based Sentinel System to Improve Compliance with Clinical Guidelines: Results of a Randomized Prospective Study." Am J Managed Care, 2005, 11, pp. 21-31. Javitt, J., J. Rebitzer and L. Reisman. Information Technology and Medical Errors: Evidence from a Randomized Trial. working paper, Case Western Reserve University, 2004. Phelps, C. E. Information Diffusion and Best Practice Adoption. in A.J. Culyer and J.P. Newhouse, eds., Handbook of Health Economics. Volume 1, Elsevier Science B.V., 2000, pp. 223 264. President s Information Technology Advisory Committee, National Coordination Office for Information. Revolutionizing Health Care Through Information Technology. Arlington, VA, June 30 2004. Rebitzer J., M. Rege and C. Shepard. Innovation, Influence and Information Technology in Health Care, working paper, Case Western Reserve University, 2005. Simon, H. "Information Storage as a Problem in Organizational Design", in Goldberg (editor), Behavioral Approaches to Modern Management, Göteborg: BAS, 1970. Skinner J. and D. Staiger."Technological Diffusion from Hybrid Corn to Beta Blockers." NBER Working Paper, 2004. Skinner J., E. Fisher and J Wennberg, The Efficiency of Medicare. NBER Working Paper Paper, 2001. 9
Table 1 (1) (2) Probit Probit Successful Resolution "Add Ace Inhibitor for Hope Qualifier Drug" Message Successful Resoltion for Any Other "Add a Drug" Message [0.141] [0.258] Study 0.130 0.035 (2.82)* (0.69) Number of patients 311 290 Number of patients in study group 155 166 Log pseudo-likelihood -154.018-158.877 In column 1, the sample consists of participants who would have qualified for an Ace inhibitor on the basis of the HOPE trial criteria, but who were not receiving the drug according to computer records. In column 2 the sample consists of participants who received an "add a drug" message other than that in column 1. The message is successfully resolved if there is evidence in the data base that the patient started the relevant drug within 270 days after the message was sent. Robust z statistics in parentheses. [ ] is mean of dep. var. in the control group in 2001 * significant at 5%; ** significant at 1% Coefficients are expressed as "derivatives". Thus in column 1 members in the study group were 13 percentage points more likely to have resolved the issue successfully than those in the control group.