Using a Human-Centered, Mixed Methods Approach to Understand the Patient Waiting Experience and its Impact on Medically Underserved Populations

Purpose: To use a mixed methods approach to investigate the patient waiting experience for a medically underserved population at an outpatient surgical clinic. Methods: We used lean methodology to perform 96 time-tracked observations of the patient journey in clinic, documenting the duration of activities from arrival to departure. We also used human-centered design (HCD) to perform and analyze 43 semi-structured interviews to understand patients’ unmet needs. Results: Patients spent an average of 68.5% of their total clinic visit waiting to be seen. While the average visit was 95.8minutes, over a quarter of visits (27%) were over 2hours. Patients waited an average of 24.4minutes in the waiting room and 41.2minutes in the exam room; and only spent 19.7% of their visit with an attending provider and 11.8% with a medical assistant. Interviews revealed that patients arrive to their visit already frustrated due to difculties related to scheduling and attending their appointment. This is exacerbated during the visit due to long wait times, perceived information opacity, and an uncomfortable waiting room, resulting in frustration and anxiety. Conclusions: While time tracking demonstrated that patients spend a majority of their visit waiting to be seen, HCD revealed that patient frustrations span the waiting experience from accessing the appointment to visit completion. These combined fndings are crucial for intervention design and implementation for medically underserved populations to improve the quality and experience with healthcare and also address system inefciencies such as long wait times

Thromboprophylaxis Is Associated With Reduced Post-hospitalization Venous Thromboembolic Events in Patients With Inflammatory Bowel Diseases

Background & Aims Patients with inflammatory bowel diseases (IBDs) have increased risk for venous thromboembolism (VTE); those who require hospitalization have particularly high risk. Few hospitalized patients with IBD receive thromboprophylaxis. We analyzed the frequency of VTE after IBD-related hospitalization, risk factors for post-hospitalization VTE, and the efficacy of prophylaxis in preventing post-hospitalization VTE. Methods In a retrospective study, we analyzed data from a multi-institutional cohort of patients with Crohn's disease or ulcerative colitis and at least 1 IBD-related hospitalization. Our primary outcome was a VTE event. All patients contributed person-time from the date of the index hospitalization to development of VTE, subsequent hospitalization, or end of follow-up. Our main predictor variable was pharmacologic thromboprophylaxis. Cox proportional hazard models adjusting for potential confounders were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results From a cohort of 2788 patients with at least 1 IBD-related hospitalization, 62 patients developed VTE after discharge (2%). Incidences of VTE at 30, 60, 90, and 180 days after the index hospitalization were 3.7/1000, 4.1/1000, 5.4/1000, and 9.4/1000 person-days, respectively. Pharmacologic thromboprophylaxis during the index hospital stay was associated with a significantly lower risk of post-hospitalization VTE (HR, 0.46; 95% CI, 0.22–0.97). Increased numbers of comorbidities (HR, 1.30; 95% CI, 1.16–1.47) and need for corticosteroids before hospitalization (HR, 1.71; 95% CI, 1.02–2.87) were also independently associated with risk of VTE. Length of hospitalization or surgery during index hospitalization was not associated with post-hospitalization VTE. Conclusions Pharmacologic thromboprophylaxis during IBD-related hospitalization is associated with reduced risk of post-hospitalization VTE.National Institutes of Health (U.S.) (U54-LM008748

Folliculin, the Product of the Birt-Hogg-Dube Tumor Suppressor Gene, Interacts with the Adherens Junction Protein p0071 to Regulate Cell-Cell Adhesion

Birt-Hogg-Dube (BHD) is a tumor suppressor gene syndrome associated with fibrofolliculomas, cystic lung disease, and chromophobe renal cell carcinoma. In seeking to elucidate the pathogenesis of BHD, we discovered a physical interaction between folliculin (FLCN), the protein product of the BHD gene, and p0071, an armadillo repeat containing protein that localizes to the cytoplasm and to adherens junctions. Adherens junctions are one of the three cell-cell junctions that are essential to the establishment and maintenance of the cellular architecture of all epithelial tissues. Surprisingly, we found that downregulation of FLCN leads to increased cell-cell adhesion in functional cell-based assays and disruption of cell polarity in a three-dimensional lumen-forming assay, both of which are phenocopied by downregulation of p0071. These data indicate that the FLCN-p0071 protein complex is a negative regulator of cell-cell adhesion. We also found that FLCN positively regulates RhoA activity and Rho-associated kinase activity, consistent with the only known function of p0071. Finally, to examine the role of Flcn loss on cell-cell adhesion in vivo, we utilized keratin-14 cre-recombinase (K14-cre) to inactivate Flcn in the mouse epidermis. The K14-Cre-Bhdflox/flox mice have striking delays in eyelid opening, wavy fur, hair loss, and epidermal hyperplasia with increased levels of mammalian target of rapamycin complex 1 (mTORC1) activity. These data support a model in which dysregulation of the FLCN-p0071 interaction leads to alterations in cell adhesion, cell polarity, and RhoA signaling, with broad implications for the role of cell-cell adhesion molecules in the pathogenesis of human disease, including emphysema and renal cell carcinoma

Improving Case Definition of Crohnʼs Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing

available in PMC 2014 June 01Background: Previous studies identifying patients with inflammatory bowel disease using administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record–based model for classification of inflammatory bowel disease leveraging the combination of codified data and information from clinical text notes using natural language processing. Methods: Using the electronic medical records of 2 large academic centers, we created data marts for Crohn’s disease (CD) and ulcerative colitis (UC) comprising patients with ≥1 International Classification of Diseases, 9th edition, code for each disease. We used codified (i.e., International Classification of Diseases, 9th edition codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables. Results: We confirmed 399 CD cases (67%) in the CD training set and 378 UC cases (63%) in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve for CD 0.95; UC 0.94) than models using only disease International Classification of Diseases, 9th edition codes (area under the curve 0.89 for CD; 0.86 for UC). Addition of natural language processing narrative terms to our final model resulted in classification of 6% to 12% more subjects with the same accuracy. Conclusions: Inclusion of narrative concepts identified using natural language processing improves the accuracy of electronic medical records case definition for CD and UC while simultaneously identifying more subjects compared with models using codified data alone.National Institutes of Health (U.S.) (NIH U54-LM008748)American Gastroenterological AssociationNational Institutes of Health (U.S.) (NIH K08 AR060257)Beth Isreal Deaconess Medical Center (Katherine Swan Ginsburg Fund)National Institutes of Health (U.S.) (NIH R01-AR056768)Burroughs Wellcome Fund (Career Award for Medical Scientists)National Institutes of Health (U.S.) (NIH U01-GM092691)National Institutes of Health (U.S.) (NIH R01-AR059648

Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence - by controlling for phylogenetic structure - for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease

Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R[superscript 2] = 0.38±0.05, and that between EHR-derived and true BPF has a mean R[superscript 2] = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10[superscript −12]). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.National Institute of General Medical Sciences (U.S.) (NIH U54-LM008748

Normalization of Plasma 25-Hydroxy Vitamin D Is Associated with Reduced Risk of Surgery in Crohn’s Disease

available in PMC 2014 August 01AB Background: Vitamin D may have an immunologic role in Crohn's disease (CD) and ulcerative colitis (UC). Retrospective studies suggested a weak association between vitamin D status and disease activity but have significant limitations. Methods: Using a multi-institution inflammatory bowel disease cohort, we identified all patients with CD and UC who had at least one measured plasma 25-hydroxy vitamin D (25(OH)D). Plasma 25(OH)D was considered sufficient at levels >=30 ng/mL. Logistic regression models adjusting for potential confounders were used to identify impact of measured plasma 25(OH)D on subsequent risk of inflammatory bowel disease-related surgery or hospitalization. In a subset of patients where multiple measures of 25(OH)D were available, we examined impact of normalization of vitamin D status on study outcomes. Results: Our study included 3217 patients (55% CD; mean age, 49 yr). The median lowest plasma 25(OH)D was 26 ng/mL (interquartile range, 17-35 ng/mL). In CD, on multivariable analysis, plasma 25(OH)D =30 ng/mL. Similar estimates were also seen for UC. Furthermore, patients with CD who had initial levels <30 ng/mL but subsequently normalized their 25(OH)D had a reduced likelihood of surgery (odds ratio, 0.56; 95% confidence interval, 0.32-0.98) compared with those who remained deficient. Conclusion: Low plasma 25(OH)D is associated with increased risk of surgery and hospitalizations in both CD and UC, and normalization of 25(OH)D status is associated with a reduction in the risk of CD-related surgery. (C) Crohn's & Colitis Foundation of America, Inc

Psychiatric co-morbidity is associated with increased risk of surgery in Crohn's disease

Psychiatric co-morbidity, in particular major depression and anxiety, is common in patients with Crohn's disease (CD) and ulcerative colitis (UC). Prior studies examining this may be confounded by the co-existence of functional bowel symptoms. Limited data exist examining an association between depression or anxiety and disease-specific endpoints such as bowel surgery.National Institutes of Health (U.S.) (NIH U54-LM008748)American Gastroenterological AssociationNational Institutes of Health (U.S.) (NIH K08 AR060257)Beth Isreal Deaconess Medical Center (Katherine Swan Ginsburg Fund)National Institutes of Health (U.S.) (NIH R01-AR056768)National Institutes of Health (U.S.) (NIH U01-GM092691)National Institutes of Health (U.S.) (NIH R01-AR059648)Burroughs Wellcome Fund (Career Award for Medical Scientists)National Institutes of Health (U.S.) (NIH K24 AR052403)National Institutes of Health (U.S.) (NIH P60 AR047782)National Institutes of Health (U.S.) (NIH R01 AR049880

Similar Risk of Depression and Anxiety Following Surgery or Hospitalization for Crohn's Disease and Ulcerative Colitis

OBJECTIVES: Psychiatric comorbidity is common in Crohn's disease (CD) and ulcerative colitis (UC). Inflammatory bowel disease (IBD)-related surgery or hospitalizations represent major events in the natural history of the disease. The objective of this study is to examine whether there is a difference in the risk of psychiatric comorbidity following surgery in CD and UC. METHODS: We used a multi-institution cohort of IBD patients without a diagnosis code for anxiety or depression preceding their IBD-related surgery or hospitalization. Demographic-, disease-, and treatment-related variables were retrieved. Multivariate logistic regression analysis was performed to individually identify risk factors for depression and anxiety. RESULTS: Our study included a total of 707 CD and 530 UC patients who underwent bowel resection surgery and did not have depression before surgery. The risk of depression 5 years after surgery was 16% and 11% in CD and UC patients, respectively. We found no difference in the risk of depression following surgery in the CD and UC patients (adjusted odds ratio, 1.11; 95% confidence interval, 0.84–1.47). Female gender, comorbidity, immunosuppressant use, perianal disease, stoma surgery, and early surgery within 3 years of care predicted depression after CD surgery; only the female gender and comorbidity predicted depression in UC patients. Only 12% of the CD cohort had ≥4 risk factors for depression, but among them nearly 44% subsequently received a diagnosis code for depression. CONCLUSIONS: IBD-related surgery or hospitalization is associated with a significant risk for depression and anxiety, with a similar magnitude of risk in both diseases.National Institutes of Health (U.S.) (U54-LM008748

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts

Background Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. Methods and Results We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. Conclusions We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.National Institutes of Health (U.S.). Informatics for Integrating Biology and the Bedside Project (U54LM008748