The new paradigm of rehabilitation : a meta-analysis of the effectiveness of incarceration-based rehabilitation in regards to recidivism reduction.

This study is a meta-analytic examination of incarceration-based rehabilitation and its ability to reduce recidivism. Substance abuse is a large problem within our convict population; many times it is a substance related conviction that is the cause of the inmates’ incarceration. Claims have been made for decades that if society can effectively rehabilitate these convicts, recidivism rates will be reduced, ultimately lowering incarceration rates. By creating a stringent criterion of inclusion, this study makes an “apples-to-apples” analysis of prior studies on the topic, examining twelve studies of incarceration-based rehabilitation and their outcome variables. The data was synthesized through meta-analytic techniques to determine if the rehabilitative efforts of correctional institutions are effective at reducing recidivism. This study found, on average, those who receive incarceration-based rehabilitation are 43% less likely to recidivate than those inmates who did not receive incarceration-based rehabilitation (p \u3c 0.0001). My hypothesis that incarceration-based rehabilitation during the era of determinate sentencing will reduce recidivism has been substantiated

Public Health Informatics in Local and State Health Agencies: An Update From the Public Health Workforce Interests and Needs Survey

OBJECTIVE: To characterize public health informatics (PHI) specialists and identify the informatics needs of the public health workforce. DESIGN: Cross-sectional study. SETTING: US local and state health agencies. PARTICIPANTS: Employees from state health agencies central office (SHA-COs) and local health departments (LHDs) participating in the 2017 Public Health Workforce Interests and Needs Survey (PH WINS). We characterized and compared the job roles for self-reported PHI, "information technology specialist or information system manager" (IT/IS), "public health science" (PHS), and "clinical and laboratory" workers. MAIN OUTCOME MEASURE: Descriptive statistics for demographics, income, education, public health experience, program area, job satisfaction, and workplace environment, as well as data and informatics skills and needs. RESULTS: A total of 17 136 SHA-CO and 26 533 LHD employees participated in the survey. PHI specialist was self-reported as a job role among 1.1% and 0.3% of SHA-CO and LHD employees. The PHI segment most closely resembled PHS employees but had less public health experience and had lower salaries. Overall, fewer than one-third of PHI specialists reported working in an informatics program area, often supporting epidemiology and surveillance, vital records, and communicable disease. Compared with PH WINS 2014, current PHI respondents' satisfaction with their job and workplace environment moved toward more neutral and negative responses, while the IT/IS, PHS, and clinical and laboratory subgroups shifted toward more positive responses. The PHI specialists were less likely than those in IT/IS, PHS, or clinical and laboratory roles to report gaps in needed data and informatics skills. CONCLUSIONS: The informatics specialists' role continues to be rare in public health agencies, and those filling that role tend to have less public health experience and be less well compensated than staff in other technically focused positions. Significant data and informatics skills gaps persist among the broader public health workforce

Measuring the impact of a health information exchange intervention on provider-based notifiable disease reporting using mixed methods: a study protocol

Background Health information exchange (HIE) is the electronic sharing of data and information between clinical care and public health entities. Previous research has shown that using HIE to electronically report laboratory results to public health can improve surveillance practice, yet there has been little utilization of HIE for improving provider-based disease reporting. This article describes a study protocol that uses mixed methods to evaluate an intervention to electronically pre-populate provider-based notifiable disease case reporting forms with clinical, laboratory and patient data available through an operational HIE. The evaluation seeks to: (1) identify barriers and facilitators to implementation, adoption and utilization of the intervention; (2) measure impacts on workflow, provider awareness, and end-user satisfaction; and (3) describe the contextual factors that impact the effectiveness of the intervention within heterogeneous clinical settings and the HIE. Methods/Design The intervention will be implemented over a staggered schedule in one of the largest and oldest HIE infrastructures in the U.S., the Indiana Network for Patient Care. Evaluation will be conducted utilizing a concurrent design mixed methods framework in which qualitative methods are embedded within the quantitative methods. Quantitative data will include reporting rates, timeliness and burden and report completeness and accuracy, analyzed using interrupted time-series and other pre-post comparisons. Qualitative data regarding pre-post provider perceptions of report completeness, accuracy, and timeliness, reporting burden, data quality, benefits, utility, adoption, utilization and impact on reporting workflow will be collected using semi-structured interviews and open-ended survey items. Data will be triangulated to find convergence or agreement by cross-validating results to produce a contextualized portrayal of the facilitators and barriers to implementation and use of the intervention. Discussion By applying mixed research methods and measuring context, facilitators and barriers, and individual, organizational and data quality factors that may impact adoption and utilization of the intervention, we will document whether and how the intervention streamlines provider-based manual reporting workflows, lowers barriers to reporting, increases data completeness, improves reporting timeliness and captures a greater portion of communicable disease burden in the community

Evaluating Methods for Identifying Cancer in Free-Text Pathology Reports Using Various Machine Learning and Data Preprocessing Approaches

Automated detection methods can address delays and incompleteness in cancer case reporting. Existing automated efforts are largely dependent on complex dictionaries and coded data. Using a gold standard of manually reviewed pathology reports, we evaluated the performance of alternative input formats and decision models on a convenience sample of free-text pathology reports. Results showed that the input format significantly impacted performance, and specific algorithms yielded better results for presicion, recall and accuracy. We conclude that our approach is sufficiently accurate for practical purposes and represents a generalized process

Variation in Information Needs and Quality: Implications for Public Health Surveillance and Biomedical Informatics

Understanding variation among users’ information needs and the quality of information in an electronic system is important for informaticians to ensure data are fit-for-use in answering important questions in clinical and public health. To measure variation in satisfaction with currently reported data, as well as perceived importance and need with respect to completeness and timeliness, we surveyed epidemiologists and other public health professionals across multiple jurisdictions. We observed consensus for some data elements, such as county of residence, which respondents perceived as important and felt should always be reported. However information needs differed for many data elements, especially when comparing notifiable diseases such as chlamydia to seasonal (influenza) and chronic (diabetes) diseases. Given the trend towards greater volume and variety of data as inputs to surveillance systems, variation of information needs impacts system design and practice. Systems must be flexible and highly configurable to accommodate variation, and informaticians must measure and improve systems and business processes to accommodate for variation of both users and information

A Vision for the Systematic Monitoring and Improvement of the Quality of Electronic Health Data

In parallel with the implementation of information and communications systems, health care organizations are beginning to amass large-scale repositories of clinical and administrative data. Many nations seek to leverage so-called Big Data repositories to support improvements in health outcomes, drug safety, health surveillance, and care delivery processes. An unsupported assumption is that electronic health care data are of sufficient quality to enable the varied use cases envisioned by health ministries. The reality is that many electronic health data sources are of suboptimal quality and unfit for particular uses. To more systematically define, characterize and improve electronic health data quality, we propose a novel framework for health data stewardship. The framework is adapted from prior data quality research outside of health, but it has been reshaped to apply a systems approach to data quality with an emphasis on health outcomes. The proposed framework is a beginning, not an end. We invite the biomedical informatics community to use and adapt the framework to improve health data quality and outcomes for populations in nations around the world

Impact of Selective Mapping Strategies on Automated Laboratory Result Notification to Public Health Authorities

Automated electronic laboratory reporting (ELR) for public health has many potential advantages, but requires mapping local laboratory test codes to a standard vocabulary such as LOINC. Mapping only the most frequently reported tests provides one way to prioritize the effort and mitigate the resource burden. We evaluated the implications of selective mapping on ELR for public health by comparing reportable conditions from an operational ELR system with the codes in the LOINC Top 2000. Laboratory result codes in the LOINC Top 2000 accounted for 65.3% of the reportable condition volume. However, by also including the 129 most frequent LOINC codes that identified reportable conditions in our system but were not present in the LOINC Top 2000, this set would cover 98% of the reportable condition volume. Our study highlights the ways that our approach to implementing vocabulary standards impacts secondary data uses such as public health reporting

Automating Provider Reporting of Communicable Disease Cases using Health Information Technology

poster abstractIntroduction Disease surveillance is a core public health (PH) function, which enables PH authorities to monitor disease outbreak and develop programs and policies to reduce disease burden. To manage and adjudicate cases of suspected communicable disease, PH workers gather data elements about persons, clinical care, and providers from various clinical sources, including providers, laboratories, among others. Current processes are paper-based and often yield incomplete and untimely reporting across different diseases requiring time-consuming follow-up by PH authorities to get needed information. Health information technology (HIT) refers to a wide range of technologies used in health care settings, including electronic health records and laboratory information systems. Health information exchange (HIE) involves electronic sharing of data and information between HIT systems, including those used in PH. Previous research has shown that using HIE to electronically report laboratory results to PH can improve surveillance practice, yet there has been little utilization of HIE for improving provider-based disease reporting [1]. Methods Our study uses an intervention to electronically pre-populate provider-based communicable disease case reporting forms with existing clinical, laboratory and patient data available through one of the largest and oldest HIE infrastructures in the U.S., the Indiana Network for Patient Care. Evaluation of the intervention will be conducted utilizing mixed methods in a concurrent design framework in which qualitative methods are embedded within the quantitative methods. Quantitative data will include reporting rates, timeliness and burden and report completeness and accuracy, analyzed using interrupted time-series and other pre-post comparisons. Qualitative data regarding pre-post provider perceptions of report completeness, accuracy, and timeliness, reporting burden, data quality, benefits, utility, adoption, utilization and impact on reporting workflow will be collected using semi-structured interviews and open-ended survey items. Data will be triangulated to find convergence or agreement by cross-validating results to produce a contextualized portrayal of the facilitators and barriers to implementation and use of the intervention. Results The intervention has been implemented in seven primary care clinics in the metropolitan Indianapolis area plus one rural clinic in Edinburgh. Analysis of baseline data shows that provider-based reports vary in their completeness, yet they contain critical information not available from laboratory information systems [2]. Furthermore, PH workers access a range of sources to gather the data they need to investigate disease cases [3]. Discussion and Conclusion By applying mixed research methods and measuring context, facilitators and barriers, and individual, organizational and data quality factors that may impact adoption and utilization of the intervention, we will document whether and how the intervention streamlines provider-based manual reporting workflows, lowers barriers to reporting, increases data completeness, improves reporting timeliness and captures a greater portion of communicable disease burden in the community. Early results are promising, and continued evaluation will be completed over the next 24 months

Evaluating the Completeness of Data Elements of Provider Reporting on Indiana's Communicable Disease Reports

poster abstractObjective To examine the completeness of data elements required for notifiable disease surveillance from official, provider-based reports submitted to a local health department. Introduction Completeness of public health information is essential for the accurate assessment of community health progress and disease surveillance. Yet challenges persist with respect to the level of completeness that public health agencies receive in reports submitted by health care providers. Missing and incomplete data can jeopardize information reliability and quality resulting in inaccurate disease evaluation and management (1). Additionally, incomplete data can prolong the time required for disease investigators to complete their work on a reported case. Thus, it is important to determine where the scarcity of information is coming from to recognize the characteristics of provider reporting. Methods Data from 1,195 unique patient cases across 7 notifiable diseases were abstracted from official reporting forms (2) submitted to a local health department serving the Indianapolis metropolitan area. The selected diseases were chlamydia, gonorrhea, syphilis, salmonella, histoplasmosis, hepatitis B-acute, and hepatitis C-chronic. Table 1 represents the duration and collection period for each of the selected diseases. Diseases were purposely chosen to represent the broad range managed by local health departments. Diseases were purposely chosen to represent the broad range managed by local health departments. A set of data elements consisting of patient, clinical, and provider information was then evaluated for completeness. The level of completeness was determined using a classification method similar to that used by Dixon et al. (3). Fields were considered complete if they contained a value; the recorded value was not validated for accuracy. Results Table 2 depicts the level of completeness for the selected data elements across the target diseases. Completeness levels and percentages varied by disease and data element with completeness being higher for patient demographic information (e.g., name, address) than provider demographics (e.g., name, clinic address). The majority of data elements for patient demographics were categorized as mostly to always complete. Conclusion It is essential that provider reports are completed in a thorough and timely manner. To increase documentation of provider information, analyses of provider characteristics such as workflow patterns, organizational constraints, and information needs are essential to understand the completeness level of provider information reporting. This will allow us to develop implementation of strategies to increase completeness of reporting across all data elements necessary to assess and investigate notifiable diseases

Lateral Fluid Percussion Injury Impairs Hippocampal Synaptic Soluble N-Ethylmaleimide Sensitive Factor Attachment Protein Receptor Complex Formation

Traumatic brain injury (TBI) and the activation of secondary injury mechanisms have been linked to impaired cognitive function, which, as observed in TBI patients and animal models, can persist for months and years following the initial injury. Impairments in neurotransmission have been well documented in experimental models of TBI, but the mechanisms underlying this dysfunction are poorly understood. Formation of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex facilitates vesicular docking and neurotransmitter release in the synaptic cleft. Published studies highlight a direct link between reduced SNARE complex formation and impairments in neurotransmitter release. While alterations in the SNARE complex have been described following severe focal TBI, it is not known if deficits in SNARE complex formation manifest in a model with reduced severity. We hypothesized that lateral fluid percussion injury (lFPI) reduces the abundance of SNARE proteins, impairs SNARE complex formation, and contributes to impaired neurobehavioral function. To this end, rats were subjected to lFPI or sham injury and tested for acute motor performance and cognitive function at 3 weeks post-injury. lFPI resulted in motor impairment between 1 and 5 days post-injury. Spatial acquisition and spatial memory, as assessed by the Morris water maze, were significantly impaired at 3 weeks after lFPI. To examine the effect of lFPI on synaptic SNARE complex formation in the injured hippocampus, a separate cohort of rats was generated and brains processed to evaluate hippocampal synaptosomal-enriched lysates at 1 week post-injury. lFPI resulted in a significant reduction in multiple monomeric SNARE proteins, including VAMP2, and α-synuclein, and SNARE complex abundance. The findings in this study are consistent with our previously published observations suggesting that impairments in hippocampal SNARE complex formation may contribute to neurobehavioral dysfunction associated with TBI