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2018 March Parkview Signature Care Thought Leader Forum
Get PDFThe agenda and supplemental documents can be accessed at this site
Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers
No full textPatient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater “in-CHC” data analytic capabilities to re-design care delivery processes for improving access and efficiency
Dashboards to Support Operational Decision Making in Health Centers: A Case for Role-Specific Design
No full textApplying a user-centered design (UCD) process within the framework of a learning healthcare system (LHS), this study unearths role-specific performance measures to support operational and financial decision making in community health centers (CHCs). We first built a multidimensional EHR/Practice Management data warehouse through a large collaborative of seven CHCs in the state of Indiana aimed at improving efficiency and access to care. A UCD process that comprised contextual interviews, card sorting and high fidelity dashboard prototyping was used to uncover over 45 different operational performance measures, many of which were unique and hitherto unknown measures of relevance to different individual roles within a CHC that included frontline staff, providers, clinical support, and executive management. Within the LHS paradigm, the study highlights the value of role-specific performance measurement and their delivery through interactive user-centered visualizations, while continuing to guide the development of future informatics tools
Youth Online - Where they may go, what they may be doing and how this may impact mental and behavioral health
No full textThis presentation will review how youth and young adults may use the Internet. We will review the different types of social tools that they use (online communities, games, applications) and the technical affordances of these platforms. We will then discuss what the technologies are used for and how people engage through them. Finally, we will assess the impact of this on the mental and behavioral health of the users, other types of outcomes including legal considerations
Data Analytics and Modeling for Appointment No-show in Community Health Centers.
Get PDFOBJECTIVES: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations.
METHODS AND MATERIALS: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models\u27 ability to identify patients missing their appointments.
RESULTS: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier).
DISCUSSION: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues.
CONCLUSION: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions
Adherence to Guideline Recommended Treatment of COPD in Discharged Patients Following an Acute Exacerbation
No full textPoster Presentation
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States, affecting more than 10 million Americans. Worldwide COPD is a disease of increasing public health importance as estimates suggest that COPD will rise from the fourth to the third most common cause of death by 2020. Increased exposure to risk factors and an aging population will attribute to even more patients with COPD. Adherence to guideline-recommended treatment regimens may decrease healthcare costs and improve patient outcomes. According to the GOLD guidelines, COPD pharmacotherapy recommendations are derived from the “ABCD” assessment tool, which takes into consideration a patient’s symptoms and their history of exacerbations (including prior hospitalizations). Symptom assessment is completed using either the Modified British Medical Research Council (mMRC) Questionnaire or the COPD Assessment Test (CAT). This is a change from the diagnostic criteria outlined in the 2010 NICE COPD Guidelines which recommend diagnosing stages of COPD based on stable-state forced expiratory volume in 1 second (FEV1). The 2017 GOLD Guidelines also place dual bronchodilators earlier than inhaled corticosteroids (ICS) in the stepwise progression of therapy based on symptoms (COPD assessment tool score) and rates of exacerbations. These changes were primarily made based on a few breakthrough studies including the FLAME and LANTERN trials which showed decreased exacerbations, improved quality of life measures, and decreased rates of pneumonia in patients on dual bronchodilators compared to LABA/ICS combinations. With these recent changes to the guidelines, it is necessary to evaluate the appropriateness of treatment regimens for patients with a diagnosis of COPD. The purpose of this study was to determine how hospitalizations for COPD exacerbation influence changes to maintenance inhaler therapy and adherence to practice guideline recommendations
Impact of clinical pathways on antibiotic prescribing in the outpatient setting
Get PDFPresentation given at Great Lakes Pharmacy Residency Conference 201
Safety vs. Surveillance: What Children have to say about Mobile Apps for Parental Control
No full textCHI \u2718: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Paper No. 124
Mobile applications ( apps ) developed to promote online safety for children are underutilized and rely heavily on parental control features that monitor and restrict their child\u27s mobile activities. This asymmetry in parental surveillance initiates an interesting research question -- how do children themselves feel about such parental control apps? We conducted a qualitative analysis of 736 reviews of 37 mobile online safety apps from Google Play that were publicly posted and written by children (ages 8-19). Our results indicate that child ratings were significantly lower than that of parents with 76% of the child reviews giving apps a single star. Children felt that the apps were overly restrictive and invasive of their personal privacy, negatively impacting their relationships with their parents. We relate these findings with HCI literature on mobile online safety, including broader literature around privacy and surveillance, and outline design opportunities for online safety apps
Systematic review of smartphone-based passive sensing for health and wellbeing.
Get PDFOBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing.
MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction.
RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges.
DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges.
CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts
Adult Consumers’ and Mental Health Professionals’ Perceptions of Telemental Health for Youth: A Delphi Study
No full textObjectives
Our objectives were to measure experts’ opinions and develop consensus via the Delphi process on the barriers, applications, and concerns associated with telemental health (TMH) for youth. Materials and methods
We delivered 3 online surveys over 2 months in Summer, 2016–2025 adult experts, including adults who experienced youth depression or suicidality, parents of youth with lived experience, and professionals (ie youth mental health researchers, clinicians/staff, or educators). We used the Delphi method to construct Likert and open-ended questions, developing expert consensus over 3 iterative surveys on the barriers and benefits of TMH for youth. Results
Adult experts identified stigma and knowledge barriers to youth mental health care. Although TMH is perceived as beneficial for screening, education, follow-up, and emotional support, no single delivery method (eg websites or instant messaging) was deemed universally beneficial. Discussion
Adults are the developers, administrators, and gatekeepers of youth mental health care. Although adult experts see potential for TMH to supplement traditional therapy via familiar technologies, there is no consensus on the technologies by which TMH should be delivered. However, there is consensus that family members and friends provide potential pathways to care; thus, an online TMH toolkit for youth would be beneficial for both caretakers and practitioners. Conclusion
Telemental health may not overcome barriers for crisis management but adult experts agreed that TMH had potential benefits for youth. Health care organizations should conduct research and provide training and education to youth caretakers and practitioners on potential barriers and benefits of TMH technologies for youth