Oropharyngeal Lesion in a 7-Year-Old Girl.

A 7-year-old girl was referred to the otolaryngology clinic for a left pharyngeal lesion noted 18 months prior. What is your diagnosis?

Person-Centered Sexual Expression: Determining Preferences of Future Nursing Home Residents.

BACKGROUND AND OBJECTIVES: Approaches to sexual expression in nursing homes are often devoid of person-centered components, such as resident choice. Little is known about residents' preferences for sexual and intimate expression across different situations. To evaluate future resident preferences, a convenience sample of 389 midlife and older adults in the United States were assessed for their perceptions of appropriateness of sexual and intimate activity among couples in nursing homes, given certain situational factors (e.g., cognitive impairment, relationship status, assent behaviors). RESEARCH DESIGN AND METHODS: A randomized experimental vignette design was implemented to determine situational factors that influence future resident preferences for sexual expression in nursing homes. Data were analyzed via multilevel modeling, allowing for multiple vignette ratings to be nested among respondents. RESULTS: Behavioral indications of assent, level of intimacy between the couple, and age of respondent affected respondents' ratings of appropriateness of sexual and intimate activities. Also, cognition and relationship levels interacted for more nuanced effects on activity appropriateness. DISCUSSION AND IMPLICATIONS: Future resident preferences are often incongruent with attitudes and common practices for approaching sexual expression in nursing home settings. This marks a unique opportunity for person-centered policy development and implementation in the realm of sexual expression.

The NIH public access policy did not harm biomedical journals.

The United States National Institutes of Health (NIH) imposed a public access policy on all publications for which the research was supported by their grants; the policy was drafted in 2004 and took effect in 2008. The policy is now 11 years old, yet no analysis has been presented to assess whether in fact this largest-scale US-based public access policy affected the vitality of the scholarly publishing enterprise, as manifested in changed mortality or natality rates of biomedical journals. We show here that implementation of the NIH policy was associated with slightly elevated mortality rates and mildly depressed natality rates of biomedical journals, but that birth rates so exceeded death rates that numbers of biomedical journals continued to rise, even in the face of the implementation of such a sweeping public access policy.

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.

Use of an explicit rule decreases procrastination in university students.

The procrastination behavior of students from a small rural university was decreased by presenting them with a rule indicating that a sooner final due date for a writing assignment would be contingent on procrastination during earlier phases of the paper. A counterbalanced AB BA design was used to measure the effects of the rule-based treatment across 2 introductory psychology classes (N = 33). Overall, participants engaged in less procrastination, missed fewer deadlines, and produced higher quality writing in the treatment condition.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.

MRI for the evaluation of knee pain: comparison of ordering practices of primary care physicians and orthopaedic surgeons.

BACKGROUND: Knee pain is one of the most common reasons for outpatient visits in the U.S. The great majority of such cases can be effectively evaluated through physical examination and judicious use of radiography. Despite this, an increasing number of magnetic resonance images (MRIs) of the knee are being ordered for patients with incomplete work-ups or for inappropriate indications. We hypothesized that MRIs ordered by orthopaedic providers were more likely to result in changes in diagnoses and/or plans for care than those ordered by non-orthopaedic providers. METHODS: We reviewed the charts of all consecutive new patients seen at our orthopaedic outpatient office between January 1, 2010, and December 31, 2011, with International Classification of Diseases, Ninth Revision (ICD-9) codes for meniscal or unspecific sprains and strains of the knee. A total of 1592 patients met our inclusion criteria and were divided into two groups: those initially evaluated and referred by their primary care physician (PCP) (n = 747) and those initially evaluated by one of our staff orthopaedic surgeons (n = 845). RESULTS: MRI-ordering rates were nearly identical between orthopaedic surgeons and PCPs (25.0% versus 24.8%; p = 0.945). MRIs ordered by orthopaedic surgeons, however, resulted in significantly more arthroscopic interventions than those ordered by PCPs (41.2% versus 31.4%; p = 0.042). Orthopaedic surgeons ordered MRIs for patients who were more likely to benefit from arthroscopic intervention, including patients who were younger (mean age, 45.1 years versus 56.5 years for those with PCP-ordered MRIs; p < 0.001), patients with acute symptoms (39.3% versus 22.2%; p < 0.001), and patients with a history of trauma (49.3% versus 36.2%; p = 0.019). Finally, orthopaedic surgeons were less likely than PCPs to order MRIs for patients with substantial osteoarthritis who subsequently underwent total knee arthroplasty (4.3% versus 9.2%; p = 0.048). CONCLUSIONS: MRI utilization by orthopaedic surgeons results in more appropriate interventions for patients with symptoms and findings most amenable to surgical intervention.

Personalised physician learning intervention to improve hypertension and lipid control: randomised trial comparing two methods of physician profiling.

PURPOSE: To assess the impact of personalised physician learning (PPL) interventions using simulated learning cases on control of hypertension and dyslipidaemia in primary care settings. METHODS: A total of 132 primary care physicians, 4568 eligible patients with uncontrolled hypertension, and 15 392 eligible patients with uncontrolled dyslipidaemia were cluster-randomised to one of three conditions: (a) no intervention, (b) PPL-electronic medical record (EMR) intervention in which 12 PPL cases were assigned to each physician based on observed patterns of care in the EMR in the previous year, or (c) PPL-ASSESS intervention in which 12 PPL cases were assigned to each physician based on their performance on four standardised assessment cases. General and generalised linear mixed models were used to account for clustering and to model differences in patient outcomes in the study arms. RESULTS: Among patients with uncontrolled hypertension at baseline, 49.1%, 46.6% and 47.3% (p=0.43) achieved blood pressure (BP) targets at follow-up. Among patients with uncontrolled dyslipidaemia at baseline, 37.5%, 37.3% and 38.1% (p=0.72) achieved low density lipoprotein cholesterol targets at follow-up in PPL-EMR, PPL-ASSESS and the control group, respectively. Although systolic (BP) (p<0.001) and lipid (p<0.001) values significantly improved during the study, the group-by-time interaction term showed no differential change in systolic BP values (p=0.51) or lipid values (p=0.61) among the three study arms. No difference in intervention effect was noted when comparing the PPL-EMR with the PPL-ASSESS intervention (p=0.47). CONCLUSIONS: The two PPL interventions tested in this study did not lead to improved control of hypertension or dyslipidaemia in primary care clinics during a mean 14-month follow-up period. This null result may have been due in part to substantial overall improvement in BP and lipid control at the study sites during the study. TRIAL REGISTRATION NUMBER: NCT00903071.

Educating resident physicians using virtual case-based simulation improves diabetes management: a randomized controlled trial.

PURPOSE: To test a virtual case-based Simulated Diabetes Education intervention (SimDE) developed to teach primary care residents how to manage diabetes. METHOD: Nineteen primary care residency programs, with 341 volunteer residents in all postgraduate years (PGY), were randomly assigned to a SimDE intervention group or control group (CG). The Web-based interactive educational intervention used computerized virtual patients who responded to provider actions through programmed simulation models. Eighteen distinct learning cases (L-cases) were assigned to SimDE residents over six months from 2010 to 2011. Impact was assessed using performance on four virtual assessment cases (A-cases), an objective knowledge test, and pre-post changes in self-assessed diabetes knowledge and confidence. Group comparisons were analyzed using generalized linear mixed models, controlling for clustering of residents within residency programs and differences in baseline knowledge. RESULTS: The percentages of residents appropriately achieving A-case composite clinical goals for glucose, blood pressure, and lipids were as follows: A-case 1: SimDE = 21.2%, CG = 1.8%, P = .002; A-case 2: SimDE = 15.7%, CG = 4.7%, P = .02; A-case 3: SimDE = 48.0%, CG = 10.4%, P < .001; and A-case 4: SimDE = 42.1%, CG = 18.7%, P = .004. The mean knowledge score and pre-post changes in self-assessed knowledge and confidence were significantly better for SimDE group than CG participants. CONCLUSIONS: A virtual case-based simulated diabetes education intervention improved diabetes management skills, knowledge, and confidence for primary care residents.

Cost-effectiveness of an electronic medical record based clinical decision support system.

BACKGROUND AND OBJECTIVE: Medical groups have invested billions of dollars in electronic medical records (EMRs), but few studies have examined the cost-effectiveness of EMR-based clinical decision support (CDS). This study examined the cost-effectiveness of EMR-based CDS for adults with diabetes from the perspective of the health care system. DATA SOURCES/SETTING: Clinical outcome and cost data from a randomized clinical trial of EMR-based CDS were used as inputs into a diabetes simulation model. The simulation cohort included 1,092 patients with diabetes with A1c above goal at baseline. STUDY DESIGN: The United Kingdom Prospective Diabetes Study Outcomes Model, a validated simulation model of diabetes, was used to evaluate remaining life years, quality-adjusted life years (QALYs), and health care costs over patient lifetimes (40-year time horizon) from the health system perspective. PRINCIPAL FINDINGS: Patients in the intervention group had significantly lowered A1c (0.26 percent, p = .014) relative to patients in the control arm. Intervention costs were $120 (SE = 45) per patient in the first year and $76 (SE = 45) per patient in the following years. In the base case analysis, EMR-based CDS increased lifetime QALYs by 0.04 (SE = 0.01) and increased lifetime costs by $112 (SE = 660), resulting in an incremental cost-effectiveness ratio of $3,017 per QALY. The cost-effectiveness of EMR-based CDS persisted in one-way, two-way, and probabilistic sensitivity analyses. CONCLUSIONS: Widespread adoption of sophisticated EMR-based CDS has the potential to modestly improve the quality of care for patients with chronic conditions without substantially increasing costs to the health care system.

Impact of electronic health record clinical decision support on diabetes care: a randomized trial.

PURPOSE: We wanted to assess the impact of an electronic health record-based diabetes clinical decision support system on control of hemoglobin A(1c) (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes. METHODS: We conducted a clinic-randomized trial conducted from October 2006 to May 2007 in Minnesota. Included were 11 clinics with 41 consenting primary care physicians and the physicians' 2,556 patients with diabetes. Patients were randomized either to receive or not to receive an electronic health record (EHR)-based clinical decision support system designed to improve care for those patients whose hemoglobin A(1c), blood pressure, or LDL cholesterol levels were higher than goal at any office visit. Analysis used general and generalized linear mixed models with repeated time measurements to accommodate the nested data structure. RESULTS: The intervention group physicians used the EHR-based decision support system at 62.6% of all office visits made by adults with diabetes. The intervention group diabetes patients had significantly better hemoglobin A(1c) (intervention effect -0.26%; 95% confidence interval, -0.06% to -0.47%; P=.01), and better maintenance of systolic blood pressure control (80.2% vs 75.1%, P=.03) and borderline better maintenance of diastolic blood pressure control (85.6% vs 81.7%, P =.07), but not improved low-density lipoprotein cholesterol levels (P = .62) than patients of physicians randomized to the control arm of the study. Among intervention group physicians, 94% were satisfied or very satisfied with the intervention, and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued. CONCLUSIONS: EHR-based diabetes clinical decision support significantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes.

Simulated physician learning program improves glucose control in adults with diabetes.

OBJECTIVE: Inexpensive and standardized methods to deliver medical education to primary care physicians (PCPs) are desirable. Our objective was to assess the impact of an individualized simulated learning intervention on diabetes care provided by PCPs. RESEARCH DESIGN AND METHODS: Eleven clinics with 41 consenting PCPs in a Minnesota medical group were randomized to receive or not receive the learning intervention. Each intervention PCP was assigned 12 simulated type 2 diabetes cases that took about 15 min each to complete. Cases were designed to remedy specific physician deficits found in their electronic medical record observed practice patterns. General linear mixed models that accommodated the cluster randomized study design were used to assess patient-level change from preintervention to 12-month postintervention of A1C, blood pressure, and LDL cholesterol. The relationship between the study arm and the total of intervention and patient health care costs was also analyzed. RESULTS: Intervention clinic patients with baseline A1C >or=7% significantly improved glycemic control at the last postintervention A1C measurement, intervention effect of -0.19% mean A1C (P = 0.034) and +6.7% in A1C <7% goal achievement (P = 0.0099). Costs trended lower, with the cost per patient -$71 (SE = 142, P = 0.63) relative to nonintervention clinic patients. The intervention did not significantly improve blood pressure or LDL control. Models adjusting for age, sex, and comorbidity showed similar results. PCPs reported high satisfaction. CONCLUSIONS: A brief individualized case-based simulated learning intervention for PCPs led to modest but significant glucose control improvement in adults with type 2 diabetes without increasing costs.

An innovative continuous flow system for monitoring heavy metal pollution in water using transgenic Xenopus laevis tadpoles.

While numerous detection methods exist for environmental heavy metal monitoring, easy-to-use technologies combining rapidity with in vivo measurements are lacking. Multiwell systems exploiting transgenic tadpoles are ideal but require time-consuming placement of individuals in wells. We developed a real-time flow-through system, based on Fountain Flow cytometry, which measures in situ contaminant-induced fluorescence in transgenic amphibian larvae immersed in water samples. The system maintains the advantages of transgenic amphibians, but requires minimal human intervention. Portable and self-contained, it allows on-site measurements. Optimization exploited a transgenic Xenopus laevis bearing a chimeric gene with metal responsive elements fused to eGFP. The transgene was selectively induced by 1 microM Zn(2+). Using this tadpole we show the continuous flow method to be as rapid and sensitive as image analysis. Flow-through readings thus accelerate the overall process of data acquisition and render fluorescent monitoring of tadpoles suitable for on-site tracking of heavy metal pollution.

Duration-related efficacy of postoperative antibiotics following pediatric tonsillectomy: a prospective, randomized, placebo-controlled trial.

OBJECTIVE: To determine whether a 3-day course of postoperative antibiotics is as effective as a 7-day course in reducing pain and reducing time to resumption of a normal diet and level of activity following pediatric tonsillectomy. DESIGN: A prospective, randomized, placebo-controlled trial. SETTING: Academic medical center. PATIENTS: Forty-nine patients were enrolled in the study. Preoperative demographic information was obtained. INTERVENTIONS: Tonsillectomy with or without adenoidectomy was performed by the senior author (J.J.) using electrocautery. Patients were randomized to receive either a 3- or 7-day course of amoxicillin. MAIN OUTCOME MEASURES: Parents were asked to record the following information: analgesic use for the first 7 postoperative days, postoperative days the child initiated his or her usual diet and level of activity, and medical treatment for oral hemorrhage or dehydration. RESULTS: Of the 49 patients, 26 were randomized to receive 7 days of postoperative antibiotics (group A) and 23 to receive 3 days of antibiotics, followed by 4 days of placebo (group B). Results were obtained for 47 of the enrolled patients (96%). No statistically significant difference was noted between the 2 groups with regard to postoperative pain or time to resumption of a normal diet and level of activity. CONCLUSION: A 3-day course of antibiotics following pediatric tonsillectomy is as effective as a 7-day course with regard to postoperative analgesic use and resumption of normal diet and level of activity. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00662987.

Customized feedback to patients and providers failed to improve safety or quality of diabetes care: a randomized trial.

OBJECTIVE: To assess whether providing customized clinical information to patients and physicians improves safety or quality of diabetes care. RESEARCH DESIGN AND METHODS: Study subjects included 123 primary care physicians and 3,703 eligible adult diabetic patients with elevated A1C or LDL cholesterol, who were randomly assigned to receive customized feedback of clinical information as follows: 1) patient only, 2) physician only, 3) both the patient and physician, or 4) neither patient nor physician. In the intervention groups, patients received customized mailed information or physicians received printed, prioritized lists of patients with recommended clinical actions and performance feedback. Hierarchical models were used to accommodate group random assignment. RESULTS: Study interventions did not improve A1C test ordering (P = 0.35) and negatively affected LDL cholesterol test ordering (P < 0.001) in the 12 months postintervention. Interventions had no effect on LDL cholesterol values (P = 0.64), which improved in all groups over time. Interventions had a borderline unfavorable effect on A1C values among those with baseline A1C >or=7% (P = 0.10) and an unfavorable effect on A1C values among those with baseline A1C >or=8% (P < 0.01). Interventions did not reduce risky prescribing events or increase treatment intensification. Time to next visit was longer in all intervention groups compared with that for the control group (P < 0.05). CONCLUSIONS: Providing customized decision support to physicians and/or patients did not improve quality or safety of diabetes care and worsened A1C control in patients with baseline A1C >or=8%. Future researchers should consider providing point-of-care decision support with redesign of office systems and/or incentives to increase appropriate actions in response to decision-support information.

Simulated physician learning intervention to improve safety and quality of diabetes care: a randomized trial.

OBJECTIVE: To assess two physician learning interventions designed to improve safety and quality of diabetes care delivered by primary care physicians (PCPs). RESEARCH DESIGN AND METHODS: This group randomized clinical trial included 57 consenting PCPs and their 2,020 eligible adult patients with diabetes. Physicians were randomized to no intervention (group A), a simulated case-based physician learning intervention (group B), or the same simulated case-based learning intervention with physician opinion leader feedback (group C). Dependent variables included A1C values, LDL cholesterol values, pharmacotherapy intensification rates in patients not at clinical goals, and risky prescribing events. RESULTS: Groups B and C had substantial reductions in risky prescribing of metformin in patients with renal impairment (P = 0.03). Compared with groups A and C, physicians in group B achieved slightly better glycemic control (P = 0.04), but physician intensification of oral glucose-lowering medications was not affected by interventions (P = 0.41). Lipid management improved over time (P < 0.001) but did not differ across study groups (P = 0.67). CONCLUSIONS: A simulated, case-based learning intervention for physicians significantly reduced risky prescribing events and marginally improved glycemic control in actual patients. The addition of opinion leader feedback did not improve the learning intervention. Refinement and further development of this approach is warranted.

Is there a downside to customizing care? Implications of general and patient-specific treatment strategies.

RATIONALE, AIMS AND OBJECTIVES: The use of general clinical guidelines versus customization of patient care presents a dilemma for clinicians managing chronic illness. The objective of this project is to investigate the claim that the performance of customized strategies for the management of chronic illness depends on accurate patient categorization, and inaccurate categorization can lead to worse performance than that achievable using a general clinical guideline. METHODS: This paper is based on an analysis of a basic utility model that differentiates between the use of general management strategies and customized strategies. RESULTS: The analysis identifies necessary conditions for preferring general strategies to customized strategies as a trade-off between strategy performance and the probability of correct patient categorization. The analysis shows that customized treatment strategies developed under optimal conditions are not necessarily preferred. CONCLUSIONS: Results of the analysis have four implications regarding the design and use of clinical guidelines and customization of care: (i) the balance between the applications of more general strategies versus customization depends on the specificity and accuracy of the strategies; (ii) adoption of clinical guidelines may be stifled as the complexity of guidelines increases to account for growing evidence; (iii) clinical inertia (i.e. the failure to intensify an indicated treatment) can be a rational response to strategy specificity and the probability of misapplication; and, (iv) current clinical guidelines and other decision-support tools may be improved if they accommodate the need for customization of strategies for some patients while providing support for proper categorization of patients.

Variation in quality of diabetes care at the levels of patient, physician, and clinic.

INTRODUCTION: We studied variance in glycated hemoglobin (HbA1c) values among adults with diabetes to identify variation in quality of diabetes care at the levels of patient, physician, and clinic, and to identify which levels contribute the most to variation and which variables at each level are related to quality of diabetes care. METHODS: Study subjects were 120 primary care physicians and their 2589 eligible adult patients with diabetes seen at 18 clinics. The dependent variable was HbA1c values recorded in clinical databases. Multivariate hierarchical models were used to partition variation in HbA1c values across the levels of patient, physician, or clinic and to identify significant predictors of HbA1c at each level. RESULTS: More than 95% of variance in HbA1c values was attributable to the patient level. Much less variance was seen at the physician and clinic level. Inclusion of patient and physician covariates did not substantially change this pattern of results. Intensification of pharmacotherapy (t = -7.40, P < .01) and patient age (t = 2.10, P < .05) were related to favorable change in HbA1c. Physician age, physician specialty, number of diabetes patients per physician, patient comorbidity, and clinic assignment did not predict change in HbA1c value. The overall model with covariates explained 11.8% of change in HbA1c value over time. CONCLUSION: These data suggest that most variance in HbA1c values is attributable to patient factors, although physicians play a major role in some patient factors (e.g., intensification of medication). These findings may lead to more effective care-improvement strategies and accountability measures.