Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip

Predictive risk models to identify people with chronic conditions at risk of hospitalisation

A disproportionately large percentage of health care costs and utilisation is spent on a small fraction of the population with complex and chronic conditions (Panattoni et al., 2011). It is widely agreed that effective and accessible primary health care (PHC) is central to reducing potentially avoidable hospitalisations (PAHs) associated with chronic disease. Predictive risk modelling is one method that is used to identify individuals who may be at risk of a hospitalisation event. The Predictive Risk Model (PRM) is a tool for identifying at-risk patients, so that appropriate preventive care can be provided, to avoid both exacerbation and complications of existing conditions, and acute events that may lead to hospitalisation. This Policy Issue Review identifies a selection of currently available PRMs, focusing on those applied in a PHC setting; and examines evidence of reliability in targeting patients with complex and chronic conditions

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Using prediction to facilitate patient flow in a health care delivery chain

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-178).A health care delivery chain is a series of treatment steps through which patients flow. The Emergency Department (ED)/Inpatient Unit (IU) chain is an example chain, common to many hospitals. Recent literature has suggested that predictions of IU admission, when patients enter the ED, could be used to initiate IU bed preparations before the patient has completed emergency treatment and improve flow through the chain. This dissertation explores the merit and implications of this suggestion. Using retrospective data collected at the ED of the Veterans Health Administration Boston Health Care System (VHA BHS), three methods are selected for making admission predictions: expert opinion, naive Bayes conditional probability and linear regression with a logit link function (logit-linear regression). The logit-linear regression is found to perform best. Databases of historic data are collected from four hospitals including VHA BHS. Logit-linear regression prediction models generated for each individual hospital perform well based on multiple measures. The prediction model generated for the VHA BHS hospital continues to perform well when predictive data are collected and coded prospectively by nurses. For two weeks, predictions are made on each patient that enters the VHA BHS ED. This data is then summarized and displayed on the VHA BHS internet homepage. No change was observed in key ED flow measures; however, interviews with hospital staff exposed ways in which the prediction information was valuable: planning individual patient admissions, personal scheduling, resource scheduling, resource alignment, and hospital network coordination. A discrete event simulation of the system shows that if IU staff emphasizes discharge before noon, flow measures improve as compared to a baseline scenario where discharge priority begins at 1pm. Sharing ED crowding or prediction information leads to best patient flow performance when using specific schedules dictating IU response to the information. This dissertation targets the practical and theoretical implications of using prediction to improve flow through the ED/IU health care delivery chain. It is suggested that the results will have impact on many other levels of health care delivery that share the delivery chain structure.by Jordan Shefer Peck.Ph.D

A big data augmented analytics platform to operationalize efficiencies at community clinics

Indiana University-Purdue University Indianapolis (IUPUI)Community Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowledge gaps in the literature and identify areas for future research in health informatics. The first RQ seeks to understand the information needs to support operations at CHCs and implement an information architecture to support those needs. The second RQ leverages the implemented data infrastructure to evaluate how advanced analytics can guide open access scheduling – a specific use case of this research. Finally, the third RQ seeks to understand how the data can be visualized to support decision making among varying roles in CHCs. Based on the unique work and information flow needs uncovered at these CHCs, an end to-end analytics solution was designed, developed and validated within the framework of a rapid learning health system. The solution comprised of a novel heterogeneous longitudinal clinic data warehouse augmented with big data technologies and dashboard visualizations to inform CHCs regarding operational priorities and to support engagement in the systems redesign initiative. Application of predictive analytics on the health center data guided the implementation of open access scheduling and up to a 15% reduction in the missed appointment rates. Performance measures of importance to specific job profiles within the CHCs were uncovered. This was followed by a user-centered design of an online interactive dashboard to support rapid assessments of care delivery. The impact of the dashboard was assessed over time and formally validated through a usability study involving cognitive task analysis and a system usability scale questionnaire. Wider scale implementation of the data aggregation and analytics platform through regional health information networks could better support a range of health system redesign initiatives in order to address the national ‘triple aim’ of healthcare

Use of workers' compensation data for occupational safety and health: proceedings from June 2012 workshop

The purpose for the June 2012 Use of Workers' Compensation Data for Occupational Safety and Health Workshop was to explore ways in which workers' compensation information can be used for public health research and surveillance. Thirty-five poster and platform presentations described studies that utilized workers' compensation information while exploring limitations of these resources. The workshop proceedings contain summary articles for the presentations plus notes from the discussion groups for the 6 white papers that were drafted for the workshop. The workshop was co-sponsored by the Bureau of Labor Statistics (BLS), Council of State and Territorial Epidemiologists (CSTE), International Association of Industrial Accident Boards and Commissions (IAIABC), National Council on Compensation Insurance (NCCI), National Institute for Occupational Safety and Health (NIOSH), Occupational Safety and Health Administration (OSHA), and the Washington State Department of Labor and Industries, Safety and Health Assessment for Research and Prevention (SHARP) program.Introduction -- Acknowledgements -- Use of workers' compensation for occupational safety and health: opening remarks -- The advantages of combining workers' compensation data with other employee databases for surveillance of occupational injuries and illnesses in hospital workers -- Safe lifting in long-term care facilities, workers' compensation savings and resident well-being -- Workers' compensation versus safety data use at the veterans health administration: uses and weaknesses -- Linking workers' compensation data and earnings data to estimate the economic consequences of workplace injuries -- Workers' compensation costs in wholesale and retail trade sectors -- Linking workers' compensation and group health insurance data to examine the impact of occupational injury on workers' and their family members' health care use and costs: two case studies -- Occupational amputations in illinois: data linkage to target interventions -- The role of professional employer organizations in workers compensation: evidence of workplace safety and reporting -- Using workers' compensation data to conduct OHS surveillance of temporary workers in Washington state -- How WorkSafeBC uses workers' compensation data for loss prevention -- Hitting the mark: improving effectiveness of high hazard industry interventions by modifying identification and targeting methodology -- Injury trends in the Ohio Workers' Compensation System -- Randomized government safety inspections reduce worker injuries with no detectable job loss -- Comparison of data sources for the surveillance of work injury -- OSHA recordkeeping practices and workers compensation claims in Washington; results from a survey of Washington BLS respondents -- Completeness of workers' compensation data in identifying work-related injuries -- Another method for comparing injury data from workers compensation and survey sources -- Using O*Net to study the relationship between psychosocial characteristics of the job and workers' compensation claims outcomes -- Impact of differential injury reporting on the estimation of the total number of work-related amputation injuries -- Exploring New Hampshire workers' compensation data for its utility in enhancing the state's occupational health surveillance system -- Using workers' compensation data for surveillance of occupational injuries and illnesses-Ohio, 2005-2009 -- Using an administrative workers' compensation claims database for occupational health surveillance in California: validation of a case classification scheme for amputations -- Describing agricultural occupational injury in Ohio using Bureau of Workers' Compensation claims -- Use of multiple data sources to enumerate work-related amputations in Massachusetts: the contribution of workers' compensation records -- Workers' compensation-related CSTE occupational health indicators -- The effectiveness of the Safety and Health Achievement Recognition Program (SHARP) in reducing the frequency and cost of workers' compensation claims -- Comparison of cost valuation methods for workers compensation data -- Development and evaluation of an auto-coding model for coding unstructured text data among workers' compensation claims -- Patterns in employees' compensation appeals board decisions: exploratory text mining and information extraction -- Identifying workers' compensation as the expected payer in emergency department medical records -- Utilizing workers' compensation data to evaluate interventions and develop business cases -- gender, age, and risk of injury in the workplace -- The mystery of more Monday soft-tissue injury claims -- Is occupational injury risk higher at new firms? -- Discussion of: Successes using workers' compensation data for health care injury prevention: surveillance, design, costs, and accuracy -- Discussion of: The total burden of work-related injuries and illnesses: a draft white paper developed for the workshop on the use of workers' compensation data -- Discussion of: Workers' compensation loss prevention: a white paper for discussion -- Discussion of: Contingent workers: data analysis limitations and strategies -- Discussion of: Using workers' compensation administrative data to analyze injury rates: a sample study with the Wisconsin Workers' Compensation Division -- Discussion of: The Role of leading indicators in the surveillance of occupational health and safety -- Final workshop discussion group -- State health agencies' access to state workers' compensation data: results of an assessment conducted by the Council of State and Territorial Epidemiologists, 2012 -- Workshop participants -- Workshop agenda -- Poster presentations.David F. Utterback and Teresa M. Schnorr, editors.May 2013.Also available via the World Wide Web as an Acrobat .pdf file (11.9 MB, 232 p.).Includes bibliographical references

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

NOIRS 2011

"The National Institute for Occupational Safety and Health (NIOSH), in partnership with the Liberty Mutual Research Institute for Safety (LMRIS) and the National Safety Council (NSC), hosted the fifth National Occupational Injury Research Symposium (NOIRS) on October 18-20, 2011 at the Waterfront Place Hotel in Morgantown, West Virginia. NOIRS is the only national forum focused on the presentation of occupational injury research findings, data, and methods. This symposium served numerous objectives aimed at preventing traumatic occupational injury through research and prevention. They included: presenting current research findings; fostering collaboration among researchers from a broad range of disciplines, perspectives, and topic areas; identifying 'best practices' for the prevention of work-related injuries; exploring the cost-effectiveness of injury prevention strategies and interventions; showcasing innovative and high technology approaches to research and prevention; and continuing to promote the implementation of the National Occupational Research Agenda (NORA). Questions addressed included: What are the latest traumatic occupational injury research findings? What are emerging problems and research areas in workplace trauma? How is prevention through design being applied to occupational injury research and prevention? What activities are being done to implement research to practice in the area of traumatic occupational injury? What are the best practice intervention and prevention strategies? What are the economic costs of traumatic occupational injuries and are the prevention strategies cost-effective? What are the trends in traumatic occupational injury and fatality incidence? In research tools, techniques, and methods? In prevention? What specific workplace risks are faced by adolescents, older adults, foreign-born workers, non-English-speaking workers, low-literacy workers, and other special populations? How can researchers and practitioners in different sectors and disciplines better collaborate and coordinate their activities to reduce traumatic occupational injuries? What methods are available to assess, quantify, and compare traumatic occupational injury risks? Occupational injury researchers from all disciplines attended and shared their research. We encouraged participation by all interested individuals, including: safety researchers; safety practitioners; health care professionals; administrators; epidemiologists; engineers; manufacturers; communication researchers; regulators; employers; policy makers; insurers; students; advocates; workers; educators and trainers; and others interested in attending. The symposium consistd of contributed oral presentations in concurrent sessions and a poster session." - NIOSHTIC-2Welcome -- Symposium information -- Agenda at a glance -- Meeting facilities-main floor -- Acknowledgements -- Full agenda -- List of opening and closing plenary speakers -- List of pre-registered participants -- Abstracts -- Poster abstracts -- Abstract reviewers"October 2011.""This year's symposium theme, Future directions in occupational injury prevention research. NOIRS would not be possible without the support of our co-sponsors: the National Safety Council and the Liberty Mutual Research Institute for Safety." - p. [1]Available via the World Wide Web as an Acrobat .pdf file (1.64 MB, 190 p.)

Use of Secure Messaging By United States Veterans and Significant Others

ABSTRACT USE OF SECURE MESSAGING BY UNITED STATES VETERANS AND SIGNIFICANT OTHERS By Claudia S. Derman The University of Wisconsin-Milwaukee, 2014 Under the Supervision of Professor Karen H. Morin, PhD, RN, ANEF, FAAN The purpose of this study was to describe the topics discussed using secure messaging (SM), the pattern of use of SM, and whether the themes discussed and/or the pattern of use varied based on gender and age of the SM user. Secure messaging is an example of a technology that focuses on patient-centered communication. Secure messaging allows patients to communicate with their clinicians using the Internet and at their convenience, while maintaining the privacy of the information exchanged. Secure messages, if approved by the patient, may also be written by family members or significant others for the patient. By its nature, the use of SM is indicative of an individual\u27s involvement in their healthcare, utilizing self-management skills. Few studies were found that reported on the content of messages written by patients or their families. No studies were found that reviewed the topics patients write about in these secure messages nor were studies found that tracked the number of messages written by patients and relating to the days and time that were most utilized. A review of 1200 secure messages written by veterans and their caregivers was undertaken to determine what information was contained within the secure messages. The 1200 messages contained 1720 themes that were grouped using content analysis to yield a total of ten topics. The day of week and the time of day of messages were additionally reviewed by gender and age of the individual. Messages written by friends of family members were reviewed and compared to those written by patients. The topic most addressed as that of medications, with more than one-third of the 1720 themes within messages relating to medications. Veterans aged 55 to 64 years were the greatest users of the SM system followed closely by those between the ages of 65 to 74. Men wrote most frequently about medications while women wrote more themes related to the topics of complaints and concerns and consultations with specialists. Pattern of use of relative to time of day and day of the week was also reviewed in subset of the sample (n= 600). The most common time frame during which messages were sent was between 9:00 a.m. and 6 p.m., accounting for more than 70% of all messages. Tuesdays and Thursdays were the most often utilized days of week for SM. The implications of this study include revisiting how MyHealtheVet is configured to enhance the veteran\u27s ability to communicate effectively and appropriately with healthcare providers. It is possible that participants employed SM rather than other identified means to contact providers as they were assured of a response within a defined period of time. Findings have implications for users, clinicians, hospital administrators, and technical staff. The purposes of SM can be revisited with users, clinicians may wish to consider alternative strategies, and administrators may wish to revisit the current structure in terms of identifying a method to sort the information contained in SM