Service Secours et feu du CERN Rapport Annuel 2003

Ce rapport résume les activités du Service Secours et Feu de la Division de l'Inspection Technique et de Sécurité pour l’année 2003. Il met l’accent sur les principaux domaines d'activité. Il contient également des statistiques détaillées

Using mobile technology to promote access, effective patient–provider communication, and adherence in underserved populations

Federally Qualified Health Centers (FQHCs) are community based centers created to provide comprehensive primary and preventive care to individuals unable to access care in the commercial medical system (e.g. related to poverty, and/or race-ethnicity). The Affordable Care Act (ACA) proposes offering Medicaid coverage to many such individuals, thus, FQHCs should receive many new Medicaid enrollees. The ACA has triggered a number of new ideas to improve affordability, health status and patient experience, commonly known as the “triple aim.” These initiatives include quality incentive programs, payment reform, and the promotion of medical homes and accountable care organizations (ACOs). These are in addition to earlier efforts to facilitate market reform, such as incentives to develop information and communication tools, such as interoperable electronic health records (EHRs) (Doebbeling, Chou \u26 Tierney, 2006). The goals of the “triple aim” cannot be met unless there is greater access to primary and preventive care. Since limited access to poor and minority populations exists today, the FQHC system could easily become overwhelmed with demand. Innovative models are essential to ensure access to needed care. If health care coverage expands beginning in 2014 as expected, FQHCs will be in a position to transition their uninsured patient population to Medicaid and state insurance exchanges. Provider networks, such as FQHCs, will be held accountable for reaching the triple aim, as measured by cost, quality, and patient experience. Recent federal and state cutbacks in the funding of Medicaid and FQHCs has led to problems with access to care and little improvement in quality of care, efficiency or satisfaction in many states. Recent “secret shopper” calls of FQHCs demonstrated access to care problems for a variety of common significant health problems at most clinics in Indiana. To investigate this problem, we have recently conducted a series of key informant interviews of clinicians, staff and leaders at three FQHCs in Indiana, regarding operational challenges and access to care. Due to current fiscal shortfalls, current patients often have difficulty in being seen and “no show\u22 cases are common. We found the leadership and clinicians at each of our participating FQHCs interested in opportunities to improve access to care and reduce no-shows, as well as strategies to improve provider-patient communication. Our findings suggest an opportunity to design and implement novel models of patient-centered care and redesign current policies and workflow to ensure that primary care is available, timely, coordinated, and cost effective. In order to address these issues, we are partnering with FQHCs and a not for profit health maintenance organization (MDwise, Inc) in Indiana to test new information and access strategies. In the proposed project, we will explore the feasibility of using mobile technology to increase access to information that will improve patient access to care and satisfaction. Short message service (SMS), or text messaging, is one way in which mobile technology has been used in healthcare. In a review of 61 studies, 50 presented findings showing a positive effect on the desired outcome from the intervention (Yeager \u26 Menachemi, 2011). Most of these studies looked at the influence of text messaging on health behaviors, however, 10 examined the impact of text messaging on administrative processes in healthcare. Nine of the ten studies found text message reminders systems reduced the no-show rates in clinics; several found text messaging was more cost effective than phone call reminders (Yeager \u26 Menachemi, 2011). The authors noted that only two of these 10 studies were conducted in primary care and none were conducted in the United States. In addition, few studies have explored the benefits of using this type of technology in vulnerable populations which are cared for by FQHCs. Thus, the present project will help to address this knowledge gap. In our project, we intend to extend beyond text messaging, into the use of social media such as Facebook and Twitter, to provide patient easy access to clinic information and ease scheduling. Other opportunities to leverage mobile technologies in community health centers that will be considered include: 1) immunization reminders; 2) management of chronic disease; 3) reduction in emergency room visits for urgent care; 4) facilitation of Medicaid reenrollment; 5) education to advance health literacy; and 6) enhance communication to improve member retention. Table 1 outlines our proposed phased approach to developing and deploying the mobile technology solution. First, we will conduct a survey to assess the feasibility of using these mobile technologies in the target population. In a recent study examined the interest in using mobile technology for appointment reminders at a safety-net clinic serving an indigent urban population (Denizard-Thompson et al, 2011). Over three hundred surveys were collected in ten days from patients who were “predominantly African-American (68 vs. 27% white, 3% Latino), and female (65% female vs. 35% male),” with a payer distribution of 24% Medicaid, 27% self-pay, 30% Medicare, and 9% privately insured (Denizard-Thompson et al, p. 458). Over half of the patients surveyed were interested in managing clinic appointments by text message (57%) and emphasized the value of surveying the clinic population to better understand its unique needs. In the proposed research, we plan to adapt the methodology and survey instrument used by Denizard-Thompson et al. Next, we propose conducting focus groups with staff, clinicians and patients in order to assess the needs from the technology and gather design ideas, selected across 5-6 FQHCs. After a prototype is developed, we will conduct a pilot test involving at least one patient from each center. Feedback from interviews conducted during this stage will be used to modify the technology as needed. Rollout to all participating centers will follow a formal training period at each clinic. Table 1 – Mobile Technology Development Plan Month Complete Study Duration/ Center Study Description 6 1 week of data collection with patients as they come to clinic for care Survey Assess patient’s current use of mobile technology and willingness to use for healthcare appointment management and alerts. 1-2 hours Needs Assessment/Formative Evaluation Focus Group/ Design Workshop with Center Staff and Care Providers 1-2 hours Needs Assessment/Formative Evaluation Focus Group/ Design Workshop with Center Patients and Caregivers 12 n/a Professional prototype development 18 Pilot Test Test technology with at least one patient, selected based on criteria deemed important by center staff and care providers. Weeks 4, 8, \u26 12: Contextual interview with patient/caregiver and center staff/ care providers 24 n/a Professional technology development Changes based on pilot testing 36 1-2 hours System Training Roll out to all participating centers In conclusion, we have found that there is a need to improve scheduling, access to care and patient-provider communication in community health centers, such as FQHCs. The proposed application of inexpensive mobile technology available on most cell phones holds promise for both improving access and ensuring higher utilization, as well as in improving patient-provider communication and adherence to current medical care guidelines. References: Doebbeling, B.N., Chou, A.F., Tierney, W.M. Priorities and Strategies for Implementation of an Integrated Informatics and Communications Technology System for Evidence-based Practices. J. Gen. Intern. Med. 21:S98-S105, 2006. Yeager, V.A., Menachemi, N. Text Messaging in Health Care: A Systematic Review of Impact Studies. Biennial Review of Health Care Management. (2011) 235-261. Denizard-Thompson, Nancy M; Feiereisel, Kirsten B; Stevens, Sheila F; Miller, David P; Wofford, James. The Digital Divide at an Urban Community Health Center: Implications for Quality Improvement and Health Care Access. Journal of community health (2011) 36: 456-460

Patient-Centered Appointment Scheduling Using Agent-Based Simulation

Enhanced access and continuity are key components of patient-centered care. Existing studies show that several interventions such as providing same day appointments, walk-in services, after-hours care, and group appointments, have been used to redesign the healthcare systems for improved access to primary care. However, an intervention focusing on a single component of care delivery (i.e. improving access to acute care) might have a negative impact other components of the system (i.e. reduced continuity of care for chronic patients). Therefore, primary care clinics should consider implementing multiple interventions tailored for their patient population needs. We collected rapid ethnography and observations to better understand clinic workflow and key constraints. We then developed an agent-based simulation model that includes all access modalities (appointments, walk-ins, and after-hours access), incorporate resources and key constraints and determine the best appointment scheduling method that improves access and continuity of care. This paper demonstrates the value of simulation models to test a variety of alternative strategies to improve access to care through scheduling

A Multidimensional Data Warehouse for Community Health Centers

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise

VizCom: A Novel Workflow Model for ICU Clinical Decision-Support

The Intensive Care Unit (ICU) has the highest annual mortality rate (4.4M) of any hospital unit or 25% of all clinical admissions. Studies show a relationship between clinician cognitive load and workflow, and their impact on patient safety and the subsequent occurrence of medical mishaps due to diagnostic error - in spite of advances in health information technology, e.g., bedside and clinical decision support (CDS) systems. The aim of our research is to: 1) investigate the root causes (underlying mechanisms) of ICU error related to the effects of clinical workflow: medical cognition, team communication/collaboration, and the use of diagnostic/CDS systems and 2) construct and validate a novel workflow model that supports improved clinical workflow, with goals to decrease adverse events, increase safety, and reduce intensivist time, effort, and cognitive resources. Lastly, our long-term objective is to apply data from aims one and two to design the next generation of diagnostic visualization-communication (VizCom) system that improves intensive care workflow, communication, and effectiveness in healthcare

Dependability and Security in Medical Information System

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Medical Information Systems (MIS) help medical practice and health care significantly. Security and dependability are two increasingly important factors for MIS nowadays. In one hand, people would be willing to step into the MIS age only when their privacy and integrity can be protected and guaranteed with MIS systems. On the other hand, only secure and reliable MIS systems would provide safe and solid medical and health care service to people. In this paper, we discuss some new security and reliability technologies which are necessary for and can be integrated with existing MISs and make the systems highly secure and dependable. We also present an implemented Middleware architecture which has been integrated with the existing VISTA/CPRS system in the U.S. Department of Veterans Affairs seamlessly and transparently

Predictive Modeling for Appointment No-show in Community Health Centers

Reducing no-show rates is one of the most important measures of access to care in Community Health Centers (CHCs). We used EMR and scheduling data to develop no-show prediction models to help design effective scheduling processes and system redesign for greater access in CHCs. Patient and provider characteristics and visit features are key elements for predicting patient adherence with an appointment

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Objectives: 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’ 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