Dean Schillinger

Bio: Dean Schillinger is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Health literacy & Health care. The author has an hindex of 72, co-authored 300 publications receiving 18196 citations. Previous affiliations of Dean Schillinger include Kaiser Permanente & Columbia University.

Association of health literacy with diabetes outcomes.

Abstract: ContextHealth literacy is a measure of patients' ability to read, comprehend, and act on medical instructions. Poor health literacy is common among racial and ethnic minorities, elderly persons, and patients with chronic conditions, particularly in public-sector settings. Little is known about the extent to which health literacy affects clinical health outcomes.ObjectivesTo examine the association between health literacy and diabetes outcomes among patients with type 2 diabetes.Design, Setting, and ParticipantsCross-sectional observational study of 408 English- and Spanish-speaking patients who were older than 30 years and had type 2 diabetes identified from the clinical database of 2 primary care clinics of a university-affiliated public hospital in San Francisco, Calif. Participants were enrolled and completed questionnaires between June and December 2000. We assessed patients' health literacy by using the short-form Test of Functional Health Literacy in Adults (s-TOFHLA) in English or Spanish.Main Outcome MeasuresMost recent hemoglobin A1c (HbA1c) level. Patients were classified as having tight glycemic control if their HbA1c was in the lowest quartile and poor control if it was in the highest quartile. We also measured the presence of self-reported diabetes complications.ResultsAfter adjusting for patients' sociodemographic characteristics, depressive symptoms, social support, treatment regimen, and years with diabetes, for each 1-point decrement in s-TOFHLA score, the HbA1c value increased by 0.02 (P = .02). Patients with inadequate health literacy were less likely than patients with adequate health literacy to achieve tight glycemic control (HbA1c ≤7.2%; adjusted odds ratio [OR], 0.57; 95% confidence interval [CI], 0.32-1.00; P = .05) and were more likely to have poor glycemic control (HbA1c ≥9.5%; adjusted OR, 2.03; 95% CI, 1.11-3.73; P = .02) and to report having retinopathy (adjusted OR, 2.33; 95% CI, 1.19-4.57; P = .01).ConclusionsAmong primary care patients with type 2 diabetes, inadequate health literacy is independently associated with worse glycemic control and higher rates of retinopathy. Inadequate health literacy may contribute to the disproportionate burden of diabetes-related problems among disadvantaged populations. Efforts should focus on developing and evaluating interventions to improve diabetes outcomes among patients with inadequate health literacy.

Closing the Loop: Physician Communication With Diabetic Patients Who Have Low Health Literacy

Abstract: Background Patients recall or comprehend as little as half of what physicians convey during an outpatient encounter. To enhance recall, comprehension, and adherence, it is recommended that physicians elicit patients' comprehension of new concepts and tailor subsequent information, particularly for patients with low functional health literacy. It is not known how frequently physicians apply this interactive educational strategy, or whether it is associated with improved health outcomes. Methods We used direct observation to measure the extent to which primary care physicians working in a public hospital assess patient recall and comprehension of new concepts during outpatient encounters, using audiotapes of visits between 38 physicians and 74 English-speaking patients with diabetes mellitus and low functional health literacy. We then examined whether there was an association between physicians' application of this interactive communication strategy and patients' glycemic control using information from clinical and administrative databases. Results Physicians assessed recall and comprehension of any new concept in 12 (20%) of 61 visits and for 15 (12%) of 124 new concepts. Patients whose physicians assessed recall or comprehension were more likely to have hemoglobin A1clevels below the mean (≤8.6%) vs patients whose physicians did not (odds ratio, 8.96; 95% confidence interval, 1.1-74.9) (P= .02). After multivariate logistic regression, the 2 variables independently associated with good glycemic control were higher health literacy levels (odds ratio, 3.97; 95% confidence interval, 1.09-14.47) (P= .04) and physicians' application of the interactive communication strategy (odds ratio, 15.15; 95% confidence interval, 2.07-110.78) (P Conclusions Primary care physicians caring for patients with diabetes mellitus and low functional health literacy rarely assessed patient recall or comprehension of new concepts. Overlooking this step in communication reflects a missed opportunity that may have important clinical implications.

Is self-efficacy associated with diabetes self-management across race/ethnicity and health literacy?

Abstract: OBJECTIVE —Although prior research demonstrated that improving diabetes self-efficacy can improve self-management behavior, little is known about the applicability of this research across race/ethnicity and health literacy levels. We examined the relationship between diabetes self-efficacy and self-management behavior in an urban, diverse, low-income population with a high prevalence of limited health literacy. RESEARCH DESIGN AND METHODS —We administered an oral questionnaire in Spanish and English to patients with type 2 diabetes at two primary care clinics at a public hospital. We measured self-efficacy, health literacy, and self-management behaviors using established instruments. We performed multivariate regressions to explore the associations between self-efficacy and self-management, adjusting for clinical and demographic factors. We tested for interactions between self-efficacy, race/ethnicity, and health literacy on self-management. RESULTS —The study participants were ethnically diverse (18% Asian/Pacific Islander, 25% African American, 42% Latino/a, and 15% white), and 52% had limited health literacy (short version of the Test of Functional Health Literacy in Adults score <23). Diabetes self-efficacy was associated with four of the five self-management domains ( P < 0.01). After adjustment, with each 10% increase in self-efficacy score, patients were more likely to report optimal diet (0.14 day more per week), exercise (0.09 day more per week), self-monitoring of blood glucose (odds ratio 1.16), and foot care (1.22), but not medication adherence (1.10, P = 0.40). The associations between self-efficacy and self-management were consistent across race/ethnicity and health literacy levels. CONCLUSIONS —Self-efficacy was associated with self-management behaviors in this vulnerable population, across both race/ethnicity and health literacy levels. However, the magnitude of the associations suggests that, among diverse populations, further study of the determinants of and barriers to self-management is warranted. Policy efforts should be focused on expanding the reach of self-management interventions to include ethnically diverse populations across the spectrum of health literacy.

Limited Literacy and Mortality in the Elderly: The Health, Aging, and Body Composition Study

Abstract: BACKGROUND: While limited literacy is common and its prevalence increases with age, no prospective study has assessed whether limited literacy is associated with mortality in older adults.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Medscape

Abstract: Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Standards of Medical Care in Diabetes—2012

Abstract: D iabetes mellitus is a chronic illness that requires continuing medical care and ongoing patient self-management education and support to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payers, and other interested individuals with the components of diabetes care, general treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. Specifically titled sections of the standards address children with diabetes, pregnant women, and people with prediabetes. These standards are not intended to preclude clinical judgment or more extensive evaluation and management of the patient by other specialists as needed. For more detailed information about management of diabetes, refer to references 1–3. The recommendations included are screening, diagnostic, and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A large number of these interventions have been shown to be cost-effective (4). A grading system (Table 1), developed by the American Diabetes Association (ADA) andmodeled after existingmethods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E. These standards of care are revised annually by the ADA’s multidisciplinary Professional Practice Committee, incorporating new evidence. For the current revision, committee members systematically searched Medline for human studies related to each subsection and published since 1 January 2010. Recommendations (bulleted at the beginning of each subsection and also listed in the “Executive Summary: Standards of Medical Care in Diabetesd2012”) were revised based on new evidence or, in some cases, to clarify the prior recommendation or match the strength of the wording to the strength of the evidence. A table linking the changes in recommendations to new evidence can be reviewed at http:// professional.diabetes.org/CPR_Search. aspx. Subsequently, as is the case for all Position Statements, the standards of care were reviewed and approved by the ExecutiveCommittee of ADA’s Board ofDirectors, which includes health care professionals, scientists, and lay people. Feedback from the larger clinical community was valuable for the 2012 revision of the standards. Readers who wish to comment on the “Standards of Medical Care in Diabetesd2012” are invited to do so at http://professional.diabetes.org/ CPR_Search.aspx. Members of the Professional Practice Committee disclose all potential financial conflicts of interest with industry. These disclosures were discussed at the onset of the standards revisionmeeting. Members of the committee, their employer, and their disclosed conflicts of interest are listed in the “Professional PracticeCommitteeMembers” table (see pg. S109). The AmericanDiabetes Association funds development of the standards and all its position statements out of its general revenues and does not utilize industry support for these purposes.