David A Asch

University of Pennsylvania, Philadelphia, Pennsylvania, United States

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Publications (233)1899.64 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Social media has been embraced as a tool for public health promotion.(1-6) However, effective strategies for harnessing the capabilities of social media remain unclear.(7-9) For example, many state and local health departments have adopted Facebook and Twitter accounts, yet public engagement with these accounts varies.(10-12) Several Web-based interventions for smoking cessation have been developed, but few tobacco prevention Web sites allow users to share links via social media.(13,14) While YouTube videos with health messages have amassed millions of views, such as a popular video targeting soft drink consumption, their long-term impact is difficult to evaluate.(15,16) In general, innovative approaches to disseminating health information must be developed to match the behavior and expectations of the public.(17) (Am J Public Health. Published online ahead of print July 17, 2014: e1-e3. doi:10.2105/AJPH.2014.302088).
    American journal of public health. 07/2014;
  • Annals of internal medicine 07/2014; · 13.98 Impact Factor
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    ABSTRACT: The purpose of this study was to describe clinicians' perceptions of interprofessional collaboration in the intensive care unit and identify factors associated with interprofessional collaboration. We performed 64 semi-structured interviews in seven hospitals with ICU nurses, physicians, respiratory therapists, nurse managers, clinical pharmacists, and dieticians. ICU clinicians perceived two distinct types of facilitators to interprofessional collaboration in critical care: cultural and structural. In the critical care setting, cultural and structural facilitators worked independently as well as in concert to create effective interprofessional collaboration. Initiatives aimed at creating and facilitating interprofessional collaboration should focus attention on cultural and structural facilitators to improve patient care and team effectiveness. © 2014 Wiley Periodicals, Inc.
    Research in Nursing & Health 07/2014; · 2.18 Impact Factor
  • Shivan J Mehta, David A Asch
    Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association 05/2014; 12(5):711-4. · 5.64 Impact Factor
  • Shivan J Mehta, David A Asch
    Cleveland Clinic Journal of Medicine 03/2014; 81(3):173-5. · 3.40 Impact Factor
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    ABSTRACT: Home wireless device monitoring could play an important role in improving the health of patients with poorly controlled chronic diseases, but daily engagement rates among these patients may be low. To test the effectiveness of two different magnitudes of financial incentives for improving adherence to remote-monitoring regimens among patients with poorly controlled diabetes. Randomized, controlled trial. (Clinicaltrials.gov Identifier: NCT01282957). Seventy-five patients with a hemoglobin A1c greater than or equal to 7.5 % recruited from a Primary Care Medical Home practice at the University of Pennsylvania Health System. Twelve weeks of daily home-monitoring of blood glucose, blood pressure, and weight (control group; n = 28); a lottery incentive with expected daily value of $2.80 (n = 26) for daily monitoring; and a lottery incentive with expected daily value of $1.40 (n = 21) for daily monitoring. Daily use of three home-monitoring devices during the three-month intervention (primary outcome) and during the three-month follow-up period and change in A1c over the intervention period (secondary outcomes). Incentive arm participants used devices on a higher proportion of days relative to control (81 % low incentive vs. 58 %, P = 0.007; 77 % high incentive vs. 58 %, P = 0.02) during the three-month intervention period. There was no difference in adherence between the two incentive arms (P = 0.58). When incentives were removed, adherence in the high incentive arm declined while remaining relatively high in the low incentive arm. In month 6, the low incentive arm had an adherence rate of 62 % compared to 35 % in the high incentive arm (P = 0.015) and 27 % in the control group (P = 0.002). A daily lottery incentive worth $1.40 per day improved monitoring rates relative to control and had significantly better efficacy once incentives were removed than a higher incentive.
    Journal of General Internal Medicine 02/2014; · 3.28 Impact Factor
  • Shivan J. Mehta, David A. Asch
    Clinical Gastroenterology and Hepatology. 01/2014; 12(5):711–714.
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    ABSTRACT: Purpose To present the principles and rationale of the Proton Priority System (PROPS), a priority points framework that assigns higher scores to patients thought to more likely benefit from proton therapy, and the distribution of PROPS scores by patient characteristics Methods and Materials We performed multivariable logistic regression to evaluate the association between PROPS scores and receipt of proton therapy, adjusted for insurance status, gender, race, geography, and the domains that inform the PROPS score. Results Among 1529 adult patients considered for proton therapy prioritization during our Center's ramp-up phase of treatment availability, PROPS scores varied by age, diagnosis, site, and other PROPS domains. In adjusted analyses, receipt of proton therapy was lower for patients with non-Medicare relative to Medicare health insurance (commercial vs Medicare: adjusted odds ratio [OR] 0.47, 95% confidence interval [CI] 0.34-0.64; managed care vs Medicare: OR 0.40, 95% CI 0.28-0.56; Medicaid vs Medicare: OR 0.24, 95% CI 0.13-0.44). Proton Priority System score and age were not significantly associated with receipt of proton therapy. Conclusions The Proton Priority System is a rationally designed and transparent system for allocation of proton therapy slots based on the best available evidence and expert opinion. Because the actual allocation of treatment slots depends mostly on insurance status, payers may consider incorporating PROPS, or its underlying principles, into proton therapy coverage policies.
    International journal of radiation oncology, biology, physics 01/2014; · 4.59 Impact Factor
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    ABSTRACT: The goal of medical education is the production of a workforce capable of improving the health and health care of patients and populations, but it is hard to use a goal that lofty, that broad, and that distant as a standard against which to judge the success of schools or training programs or particular elements within them. For that reason, the evaluation of medical education often focuses on elements of its structure and process, or on the assessment of competencies that could be considered intermediate outcomes. These measures are more practical because they are easier to collect, and they are valuable when they reflect activities in important positions along the pathway to clinical outcomes. But they are all substitutes for measuring whether educational efforts produce doctors who take good care of patients.The authors argue that the evaluation of medical education can become more closely tethered to the clinical outcomes medical education aims to achieve. They focus on a specific clinical outcome-maternal complications of obstetrical delivery-and show how examining various observable elements of physicians' training and experience helps reveal which of those elements lead to better outcomes. Does it matter where obstetricians trained? Does it matter how much experience they have? Does it matter how good they were to start? Each of these questions reflects a component of the production of a good obstetrician and, most important, defines a good obstetrician as one whose patients in the end do well.
    Academic medicine: journal of the Association of American Medical Colleges 11/2013; · 2.34 Impact Factor
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    ABSTRACT: To use the natural experiment of health insurance reform in Massachusetts to study the impact of increased insurance coverage on ICU utilization and mortality. Population-based cohort study. Massachusetts and four states (New York, Washington, Nebraska, and North Carolina) that did not enact reform. All nonpregnant nonelderly adults (age 18-64 yr) admitted to nonfederal acute care hospitals in one of the five states of interest were eligible, excluding patients who were not residents of a respective state at the time of admission. We used a difference-in-differences approach to compare trends in ICU admissions and outcomes of in-hospital mortality and discharge destination for ICU patients. Healthcare reform in Massachusetts was associated with a decrease in ICU patients without insurance from 9.3% to 5.1%. There were no significant changes in adjusted ICU admission rates, mortality, or discharge destination. In a sensitivity analysis excluding a state that enacted Medicaid reform prior to the study period, our difference-in-differences analysis demonstrated a significant increase in mortality of 0.38% per year (95% CI, 0.12-0.64%) in Massachusetts, attributable to a greater per-year decrease in mortality postreform in comparison states (-0.37%; 95% CI, -0.52% to -0.21%) compared with Massachusetts (0.01%; 95% CI, -0.20% to 0.11%). Massachusetts healthcare reform increased the number of ICU patients with insurance but was not associated with significant changes in ICU use or discharge destination among ICU patients. Reform was also not associated with changed in-hospital mortality for ICU patients; however, this association was dependent on the comparison states chosen in the analysis.
    Critical care medicine 11/2013; · 6.37 Impact Factor
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    ABSTRACT: On October 1, 2013, without a continuing resolution in place to support its budget, the U.S. federal government partially closed. One of many effects of the government shutdown was the defunding of the Children's Hospitals Graduate Medical Education (CHGME) Payment Program. Fifty-five freestanding children's hospitals currently receive CHGME funds. These hospitals train almost 30% of the general pediatricians, 44% of the pediatric medical and surgical subspecialists, and the majority of the pediatric physician-researchers in the United States.(1),(2) Capable of providing highly specialized care for pediatric patients with complex and acute conditions, freestanding children's hospitals are at the apex of . . .
    New England Journal of Medicine 10/2013; · 51.66 Impact Factor
  • David A Asch, Sean Nicholson, Marko Vujicic
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    ABSTRACT: In November 1636, the prices of tulip bulbs in the Dutch market rose rapidly from their normal level to the point where a single bulb might sell for 10 times the annual earnings of a typical worker. Just as quickly, in May 1637, tulip-bulb prices returned to their previous values. The causes of this dramatic rise and fall remain in dispute. The event occurred during the Dutch Golden Age, when stock exchanges, central banking, and many of the fundamental structures that govern contemporary capital markets and the approaches deployed by MBAs today were developed. One modern economic analysis suggests that . . .
    New England Journal of Medicine 10/2013; · 51.66 Impact Factor
  • Alison M Buttenheim, David A Asch
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    ABSTRACT: Herd immunity against vaccine-preventable diseases is a public good because it is both non-excludable (meaning that there is no way to exclude people from using it) and non-rivalrous (meaning that one person's use does not limit or restrict others' use). Like other public goods, such as lighthouses, street lights and national defense, herd immunity is vulnerable to the "free rider" problem. We discuss four conventional responses to the free rider problem (participation mandates, exclusion, incentives, and social norms) and highlight how a public good perspective can inform the design of interventions to increase vaccine acceptance.
    Human vaccines & immunotherapeutics. 10/2013; 9(12).
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    ABSTRACT: Rationale: The aging population may strain intensive care unit (ICU) capacity and adversely affect patient outcomes. Existing fluctuations in demand for ICU care offer an opportunity to explore such relationships. Objectives: To determine whether transient increases in ICU strain influence patient mortality, and identify characteristics of ICUs that are resilient to surges in capacity strain. Methods: Retrospective cohort study of 264,401 patients admitted to 155 U.S. ICUs from 2001-2008. We used logistic regression to examine relationships of measures of ICU strain (census, average acuity, and proportion of new admissions) near the time of ICU admission with mortality. Measurements and Main Results: 36,465 (14%) patients died in the hospital. ICU census on the day of a patient's admission was associated with increased mortality (OR: 1.02 per SD-unit increase (95% CI: 1.00, 1.03)). This effect was greater among ICUs employing closed (OR: 1.07 (95% CI: 1.02, 1.12)) versus open (OR: 1.01 (95% CI: 0.99, 1.03)) physician staffing models (interaction p-value=0.02). The relationship between census and mortality was stronger when the census was comprised of higher acuity patients (interaction p-value<0.01). Averaging strain over the first three days of patients' ICU stays yielded similar results except that the proportion of new admissions was now also associated with mortality (OR: 1.04 for each 10% increase (95% CI: 1.02, 1.06)). Conclusions: Several sources of ICU strain are associated with small but potentially important increases in patient mortality, particularly in ICUs employing closed staffing models. Although closed ICUs may promote favorable outcomes under static conditions, they are susceptible to being overwhelmed by patient influxes.
    American Journal of Respiratory and Critical Care Medicine 08/2013; · 11.04 Impact Factor
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    ABSTRACT: IMPORTANCE As health information technology grows, secondary uses of personal health information offer promise in advancing research, public health, and health care. Public perceptions about sharing personal health data are important for establishing and evaluating ethical and regulatory structures to oversee the use of these data. OBJECTIVE To measure patient preferences about sharing their electronic health information for secondary purposes (other than their own health care). DESIGN, SETTING, AND PARTICIPANTS In this conjoint analysis study, we surveyed 3336 adults (568 Hispanic, 500 non-Hispanic African American, and 2268 non-Hispanic white); participants were randomized to 6 of 18 scenarios describing secondary uses of electronic health information, constructed with 3 attributes: uses (research, quality improvement, or commercial marketing), users (university hospitals, commercial enterprises, or public health departments), and data sensitivity (whether it included genetic information about their own cancer risk). This design enabled participants to reveal their preferences for secondary uses of their personal health information. MAIN OUTCOMES AND MEASURES Participants responded to each conjoint scenario by rating their willingness to share their electronic personal health information on a 1 to 10 scale (1 represents low willingness; 10, high willingness). Conjoint analysis yields importance weights reflecting the contribution of a dimension (use, user, or sensitivity) to willingness to share personal health information. RESULTS The use of data was a more important factor in the conjoint analysis (importance weight, 64.3%) than the user (importance weight, 32.6%) and data sensitivity (importance weight, 3.1%). In unadjusted linear regression models, marketing uses (β = -1.55), quality improvement uses (β = -0.51), drug company users (β = -0.80), and public health department users (β = -0.52) were associated with less willingness to share health information than research uses and university hospital users (all P < .001). Hispanics and African Americans differentiated less than whites between uses. CONCLUSIONS AND RELEVANCE Participants cared most about the specific purpose for using their health information, although differences were smaller among racial and ethnic minorities. The user of the information was of secondary importance, and the sensitivity was not a significant factor. These preferences should be considered in policies governing secondary uses of health information.
    JAMA Internal Medicine 08/2013; · 10.58 Impact Factor
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    ABSTRACT: Crowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research. PubMed, Embase, and CINAHL through March 2013. Primary peer-reviewed literature that used crowdsourcing for health research. Two authors independently screened studies and abstracted data, including demographics of the crowd engaged and approaches to crowdsourcing. Twenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. These studies collectively engaged a crowd of >136,395 people, yet few studies reported demographics of the crowd. Only one (5 %) reported age, sex, and race statistics, and seven (33 %) reported at least one of these descriptors. Most reports included data on crowdsourcing logistics such as the length of crowdsourcing (n = 18, 86 %) and time to complete crowdsourcing task (n = 15, 71 %). All articles (n = 21, 100 %) reported employing some method for validating or improving the quality of data reported from the crowd. Gray literature not searched and only a sample of online survey articles included. Utilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods.
    Journal of General Internal Medicine 07/2013; · 3.28 Impact Factor
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    ABSTRACT: Many pediatric practices have adopted vaccine policies that require parents who refuse to vaccinate according to the ACIP schedule to find another health care provider. Such policies may inadvertently cluster unvaccinated patients into practices that tolerate non vaccination or alternative schedules, turning them into risky pockets of low herd immunity. The objective of this study was to assess the effect of provider zero-tolerance vaccination policies on the clustering of intentionally unvaccinated children. We developed an agent-based model of parental vaccine hesitancy, provider non-vaccination tolerance, and selection of patients into pediatric practices. We ran 84 experiments across a range of parental hesitancy and provider tolerance scenarios. When the model is initialized, all providers accommodate refusals and intentionally unvaccinated children are evenly distributed across providers. As provider tolerance decreases, hesitant children become more clustered in a smaller number of practices and eventually are not able to find a practice that will accept them. Each of these effects becomes more pronounced as the level of hesitancy in the population rises. Heterogeneity in practice tolerance to vaccine hesitant parents has the unintended result of concentrating susceptible individuals within a small number of tolerant practices, while providing little if any compensatory protection to adherent individuals. These externalities suggest an agenda for stricter policy regulation of individual practice decisions.
    Human vaccines & immunotherapeutics. 07/2013; 9(8).
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    ABSTRACT: Many programs being implemented by US employers, insurers, and health care providers use incentives to encourage patients to take better care of themselves. We critically review a range of these efforts and show that many programs, although well-meaning, are unlikely to have much impact because they require information, expertise, and self-control that few patients possess. As a result, benefits are likely to accrue disproportionately to patients who already are taking adequate care of their health. We show how these programs could be made more effective through the use of insights from behavioral economics. For example, incentive programs that offer patients small and frequent payments for behavior that would benefit the patients, such as medication adherence, can be more effective than programs with incentives that are far less visible because they are folded into a paycheck or used to reduce a monthly premium. Deploying more-nuanced insights from behavioral economics can lead to policies with the potential to increase patient engagement and deliver dividends for patients and favorable cost-effectiveness ratios for insurers, employers, and other relevant commercial entities.
    Health Affairs 07/2013; 32(7):1244-50. · 4.64 Impact Factor
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    ABSTRACT: BACKGROUND: Social media and mobile applications that allow people to work anywhere are changing the way people can contribute and collaborate. OBJECTIVE: We sought to determine the feasibility of using mobile workforce technology to validate the locations of automated external defibrillators (AEDs), an emergency public health resource. METHODS: We piloted the use of a mobile workforce application, to verify the location of 40 AEDs in Philadelphia county. AEDs were pre-identified in public locations for baseline data. The task of locating AEDs was posted online for a mobile workforce from October 2011 to January 2012. Participants were required to submit a mobile phone photo of AEDs and descriptions of the location. RESULTS: Thirty-five of the 40 AEDs were identified within the study period. Most, 91% (32/35) of the submitted AED photo information was confirmed project baseline data. Participants also provided additional data such as business hours and other nearby AEDs. CONCLUSIONS: It is feasible to engage a mobile workforce to complete health research-related tasks. Participants were able to validate information about emergency public health resources.
    Emergency Medicine Journal 05/2013; · 1.65 Impact Factor
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    ABSTRACT: Chinese translation Data on the effectiveness of employer-sponsored financial incentives for employee weight loss are limited. To test the effectiveness of 2 financial incentive designs for promoting weight loss among obese employees. Randomized, controlled trial. (ClinicalTrials.gov: NCT01208350) Children's Hospital of Philadelphia. 105 employees with a body mass index between 30 and 40 kg/m2. 24 weeks of monthly weigh-ins (control group; n = 35); individual incentive, designed as $100 per person per month for meeting or exceeding weight-loss goals (n = 35); and group incentive, designed as $500 per month split among participants within groups of 5 who met or exceeded weight-loss goals (n = 35). Weight loss after 24 weeks (primary outcome) and 36 weeks and changes in behavioral mediators of weight loss (secondary outcomes). Group-incentive participants lost more weight than control participants (mean between-group difference, 4.4 kg [95% CI, 2.0 to 6.7 kg]; P < 0.001) and individual-incentive participants (mean between-group difference, 3.2 kg [CI, 0.9 to 5.5 kg]; P = 0.008). Twelve weeks after incentives ended and after adjustment for 3-group comparisons, group-incentive participants maintained greater weight loss than control group participants (mean between-group difference, 2.9 kg [CI, 0.5 to 5.3 kg]; P = 0.016) but not greater than individual-incentive participants (mean between-group difference, 2.7 kg [CI, 0.4 to 5.0 kg]; P = 0.024). Single employer and short follow-up. A group-based financial incentive was more effective than an individual incentive and monthly weigh-ins at promoting weight loss among obese employees at 24 weeks. National Institute on Aging.
    Annals of internal medicine 04/2013; 158(7):505-14. · 13.98 Impact Factor

Publication Stats

5k Citations
1,899.64 Total Impact Points


  • 1993–2014
    • University of Pennsylvania
      • • Department of Radiation Oncology
      • • Center for Health Equity Research
      • • Department of Medicine
      • • Center for Bioethics
      • • The Wharton School
      • • Division of General Internal Medicine
      Philadelphia, Pennsylvania, United States
  • 2012
    • Dartmouth College
      Hanover, New Hampshire, United States
  • 2007–2012
    • U.S. Department of Veterans Affairs
      • Center for Health Equity Research and Promotion (CHERP)
      Washington, Washington, D.C., United States
  • 1998–2012
    • Carnegie Mellon University
      • • Department of Social and Decision Sciences
      • • Department of Engineering and Public Policy
      Pittsburgh, PA, United States
  • 1991–2012
    • Hospital of the University of Pennsylvania
      • • Department of Biostatistics and Epidemiology
      • • Department of Medicine
      • • Department of Obstetrics and Gynecology
      • • Department of General Internal Medicine
      Philadelphia, Pennsylvania, United States
  • 2011
    • University of Pittsburgh
      • Section of Palliative Care and Medical Ethics
      Pittsburgh, PA, United States
  • 2010
    • Robert Wood Johnson Foundation
      Princeton, New Jersey, United States
  • 2009
    • University of North Texas HSC at Fort Worth
      • Department of Health Management and Policy
      Fort Worth, TX, United States
  • 2001–2009
    • University of Michigan
      • Division of Pulmonary and Critical Care Medicine
      Ann Arbor, MI, United States
  • 2008
    • University of Texas - Pan American
      • Department of Economics & Finance
      Edinburg, Texas, United States
  • 2001–2008
    • The Philadelphia Center
      • Philadelphia Veterans Administration Medical Center
      Philadelphia, Pennsylvania, United States
  • 2006
    • National Institute on Aging
      Baltimore, Maryland, United States
  • 2004
    • University of Toledo
      • Division of General Internal Medicine
      Toledo, OH, United States
  • 2003
    • Johns Hopkins University
      • Division of General Internal Medicine
      Baltimore, MD, United States
  • 2002
    • Treatment Research Institute, Philadelphia PA
      Philadelphia, Pennsylvania, United States
  • 1999
    • University of Toronto
      Toronto, Ontario, Canada
  • 1995–1997
    • Minneapolis Veterans Affairs Hospital
      Minneapolis, Minnesota, United States
    • University of Chicago
      • Section of General Internal Medicine
      Chicago, IL, United States
  • 1996
    • Spokane VA Medical Center
      Spokane, Washington, United States
    • University of Miami
      • Department of Management
      Coral Gables, FL, United States