Purpose: Prescription medication costs represent more than 10% of American healthcare costs and are continuing to increase (CMS 2010). Substituting generic drugs in place of brand-name ones would result in considerable cost savings. Generics also have lower out-of-pocket expenses for patients and are associated with better adherence. Point-of-care electronic decision support in electronic health records (EHR) could affect clinician prescribing patterns. This study, however, is designed to evaluate a much simpler health information technology intervention, i.e., a user interface redesign.

Method: At our institution, the electronic prescribing interface was redesigned so that all medication searches defaulted to a generic equivalent if available, even if the provider had searched using a brand name. However, providers still had the option of selecting the brand medication through one extra mouse-click. In many domains, setting one option as the default markedly increases the chance it will be chosen (Johnson and Goldstein, Science2003). To determine whether this default setting would have as strong an effect among physicians in a practice setting, we conducted a retrospective before-after study of new outpatient prescriptions written during the year before and the year after the redesign.

Result: 886 clinicians wrote nearly 1 million new prescriptions during the two years. Generics made up 28.2% of newly prescribed medications before the change, more than doubling (65.2%) after the redesign. Only 2.1% of medications with generic equivalents were still prescribed as brands. The large increase in generic prescribing remained in regression models of the pre-post change that controlled for patient characteristics.

Conclusion: A relatively simple interface change led to a dramatic change in physician decision-making about generic drugs. Generic names are generally difficult to recall compared to strategically named, marketed and memorable brand-name drugs.The simple user interface redesign removed the onus of memorizing tedious generic names and offered a seamless workflow, steering clinicians towards generic equivalents. Further refinements are needed to ensure that physicians are not directed toward the generic option when it is less than appropriate, for example, when the generic has a narrower therapeutic index than the brand option. Such well-designed “choice environments” (Thaler and Sunstein 2008) can facilitate optimal choices without adding the cognitive burden or distractions that are typically associated with electronic decision support alerts.

Purpose: Reducing the prevalence of overdiagnosis and overtreatment has become a priority in light of rising healthcare costs.  As one clinical example, otherwise healthy infants with excessive regurgitation and crying are often treated for Gastroesophageal Reflex Disease (GERD), even though symptoms usually resolve spontaneously and medications are no more effective than placebo. In light of these facts, it is unclear why the treatment of GERD persists.  In the present research, we examined whether overtreatment persists in part because the physician’s assessment of the symptoms—in particular, use of the diagnostic label “GERD”— increases parents’ perceived need for medical interventions. 

Method: 275 parents in the waiting room of a general pediatrics clinic were asked to read a scenario that described an infant who cried and spit up excessively.  In the scenario, the infant either received a diagnosis of GERD, or the doctor referred to the symptoms as “this problem” with no mention of a formal diagnosis.  Additionally, half of parents were told that existing medications are ineffective at treating symptoms, and the rest were given no effectiveness information.  This resulted in a 2 (GERD diagnosis: present vs. absent) X 2 (Medicine ineffectiveness: present vs. absent) design.  Outcome measures included parent interest in using medication, and beliefs about whether the infant would get better without medication.

Result: When parents received no GERD diagnosis, they were interested in using medications when they assumed that the medications were effective (M=2.45; scale=0-4), but were less interested when told that medications were not effective (M=1.42; F(1,86)=12.61, p=.001).  By contrast, parents who received a GERD diagnosis were interested in using medications regardless of whether they were explicitly told that those medications were ineffective (M=2.55), or not (M=2.46; p=.70).  Moreover, all parents were told that their infant would get better without medications, but parents were less likely to believe this when they were given a diagnosis (M=3.02) compared to when there was no diagnosis (M=3.48; F(1,171)=3.95, p<.05). 

Conclusion: Labeling an otherwise normal infant as having a “disease” increased parents’ interest in medicating their infant, and led parents to believe that medication was necessary regardless of stated treatment effectiveness.  These findings suggest that doctors may inadvertently perpetuate the use of needless medical interventions by using diagnostic labels that increase demand for treatment.

Purpose: Surrogate decision makers for critically ill patients experience strong negative emotional states. Emotions influence risk perception, risk preferences, and decision making. We sought to explore the effect of emotional state and physician communication behaviors on surrogates’ life-sustaining treatment (LST) decisions. 

Method: We conducted a 5x2 between-subject randomized factorial experiment, administered via the web to community-based participants 35 and older who self-identified as the surrogate for a parent or spouse. The survey involved the hypothetical situation in which their spouse or parent has been admitted to the ICU and is receiving LST and included an interactive video meeting with an intensivist. We used block random assignment to emotional priming using a photo of the surrogate’s spouse/parent versus no priming and each of 4 physician communication behaviors during the meeting (emotion handling [yes/no], framing the decision maker [patient/surrogate], framing the default [no cardiopulmonary resuscitation (CPR)/CPR], framing the alternative to CPR [allow natural death (AND)/do not resuscitate (DNR)]). The primary outcome measure was the surrogate’s code status decision (CPR vs. DNR/AND); seconary outcomes included surrogate short form profile of mood states (POMS), decisional conflict scale (DCS), confidence that the decision would be concordant with the spouse/parent's decision, and actual concordance.

Results: 256/373 (69%) respondents logged-in and were randomized. Their average age was 50, 70% were surrogates for a parent, 63.5% were women, 76% were white, 11% black, and 9% Asian, and 81% were college educated. When asked about code status, 56% chose CPR. Emotion priming increased depression-dejection (β=1.76 [0.58 – 2.94]), but did not influence CPR choice. Physician emotion handling and framing the decision as the patient’s rather than the surrogate’s did not influence CPR choice. Framing no CPR as the default rather than CPR resulted in fewer surrogates choosing CPR (48% vs. 64%, OR=0.52 [0.32-0.87]), as did framing the alternative to CPR as AND rather than DNR (49% vs. 61%, OR=0.58 [95% CI 0.35-0.96]). Surrogates who were randomized to the emotion priming condition were more confident in their code status decision if the physician used emotion handling language than if he didn’t (OR=0.45, p = 0.036). None of the experimental conditions impacted decisional conflict or concordance.

Conclusion: Experimentally-induced emotional state did not influence code status decisions, although small changes in physician communication behaviors substantially influenced this decision.

Purpose:    The United States Preventive Services Task Force (USPSTF) makes recommendations for 60 distinct clinical services, but clinicians rarely have time to fully implement the recommendations.  A systematic approach to prioritizing and personalizing guidelines for individual patients may improve outcomes.

Methods:    We created a state transition Markov model for each of the 25 USPSTF Grade A and B guidelines for non-pregnant adults.  For each guideline, we included factors to personalize the expected benefits and risks at the patient level, based on individual patient characteristics (e.g., smoking status, hypertension, and obesity), medical history, and family history.  We personalized national life expectancy curves for a patient’s age, race, and gender, to estimate how much longer an individual would be expected to live from following each preventive care recommendation.  We rank-ordered recommendations based on expected number of life-years gained, to help identify the preventive care guidelines with the greatest benefit for each patient.

Results:    For a 62 year-old obese (height=68 inches, weight=200 lbs., BMI=30.4) male smoker with high cholesterol (TC=300, LDL=250), hypertension (BP=150/90) and family history of colorectal cancer (≥2 family members), the model's rank order of recommendations would be to quit smoking (3.1 life-years gained), lose weight (16 lbs., +1.6 life-years), lower blood pressure (to 120/80, +0.8 life years), eat a healthier diet (+0.3 life-years), lower cholesterol (to TC=199, LDL=108, +0.3 life-years), use aspirin daily (+0.1 life-years), and undergo colonoscopy (every 10 years, +0.1 life-years).  Therefore, quitting smoking would confer about 1.9x the life expectancy gain as losing weight and 3.7x the life expectancy gain as lowering blood pressure.  Expected gains from colonoscopy and use of aspirin would be similar, about 0.1x the life expectancy gain as losing weight.    For the same individual who also had uncontrolled type II diabetes (HbA1c=8), the model’s top recommendation would be to get diabetes under control (to HbA1c≤7, +1.7 life-years). Quitting smoking would still confer about 1.9x the life expectancy gain as losing weight (+1.6 vs. +0.8 life-years), but now only 1.2x the life expectancy gain as lowering blood pressure (+1.6 vs. +1.3 life-years).

Conclusion:    Quantitative models could help generate rank order recommendations of personalized preventive care.  Future studies should consider patient adherence to recommendations and determine whether personalized preventive care would improve patient outcomes and save time for providers.

Purpose: Vaccination is the most efficient and cost effective method to prevent influenza, reducing morbidity and mortality rates not only for those vaccinated, but also for the entire population by reducing the spread of the virus. In the context of contact network epidemiology, an individual who is located in the center of the network is more likely to become infected. Thus, vaccinating such individuals before others would be more efficient in reducing the influenza burden.

Method: We offer a practical way to identify the central people by using accessible data; we show that immunizing those who have been infected in the previous season, especially before the peak of the disease, can substantially reduce infection rates for a wide range of influenza viruses. It is achieved by running 2.1 million computerized simulations. Using the Susceptible Infected Recovered (SIR) compartmental model, each simulation reflected two successive influenza seasons over a 1.5 million population contact network based on the Portland population. The second season in each simulation was checked twice: when a Random Vaccination Policy (RVP) was applied and when using a vaccination policy prioritizing first those who were infected in the previous season especially before the peak (PFIP). The number of infected individuals in the two policies (RVP&PFIP) was calculated to determine the conditions where one policy is preferred to another.

Result: Results suggest that when no vaccination is offered, individuals who became infected in the previous season have a higher probability of becoming infected in the following season. Accordingly, PFIP can reduce the number of infected by up to 80% compared to RVP. Moreover, even if the cross-antigenisity rate between the viruses of two seasons is as high as 60-80%, a policy prioritizing those who became ill in the previous season is superior. We provide a simple managerial tool describing the conditions when each policy should be used.

Conclusion: No CDC recommendations have ever considered the effect of a previous season on an individual in determining a future vaccination policy for him. On a practical basis, applying the PFIP can be achieved easily by sending pamphlets, telephone reminders or even family doctor recommendations to those who were diagnosed by the family doctor as suffering from influenza like illness (ILI) in the previous season.