Effect of a Computer-Based Decision Support Intervention on Autism Spectrum Disorder Screening in Pediatric Primary Care Clinics: A Cluster Randomized Clinical Trial

Importance: Universal early screening for autism spectrum disorder (ASD) is recommended but not routinely performed. Objective: To determine whether computer-automated screening and clinical decision support can improve ASD screening rates in pediatric primary care practices. Design, Setting, and Participants: This cluster randomized clinical trial, conducted between November 16, 2010, and November 21, 2012, compared ASD screening rates among a random sample of 274 children aged 18 to 24 months in urban pediatric clinics of an inner-city county hospital system with or without an ASD screening module built into an existing decision support software system. Statistical analyses were conducted from February 6, 2017, to June 1, 2018. Interventions: Four clinics were matched in pairs based on patient volume and race/ethnicity, then randomized within pairs. Decision support with the Child Health Improvement Through Computer Automation system (CHICA) was integrated with workflow and with the electronic health record in intervention clinics. Main Outcomes and Measures: The main outcome was screening rates among children aged 18 to 24 months. Because the intervention was discontinued among children aged 18 months at the request of the participating clinics, only results for those aged 24 months were collected and analyzed. Rates of positive screening results, clinicians' response rates to screening results in the computer system, and new cases of ASD identified were also measured. Main results were controlled for race/ethnicity and intracluster correlation. Results: Two clinics were randomized to receive the intervention, and 2 served as controls. Records from 274 children (101 girls, 162 boys, and 11 missing information on sex; age range, 23-30 months) were reviewed (138 in the intervention clinics and 136 in the control clinics). Of 263 children, 242 (92.0%) were enrolled in Medicaid, 138 (52.5%) were African American, and 96 (36.5%) were Hispanic. Screening rates in the intervention clinics increased from 0% (95% CI, 0%-5.5%) at baseline to 68.4% (13 of 19) (95% CI, 43.4%-87.4%) in 6 months and to 100% (18 of 18) (95% CI, 81.5%-100%) in 24 months. Control clinics had no significant increase in screening rates (baseline, 7 of 64 children [10.9%]; 6-24 months after the intervention, 11 of 72 children [15.3%]; P = .46). Screening results were positive for 265 of 980 children (27.0%) screened by CHICA during the study period. Among the 265 patients with positive screening results, physicians indicated any response in CHICA in 151 (57.0%). Two children in the intervention group received a new diagnosis of ASD within the time frame of the study. Conclusions and Relevance: The findings suggest that computer automation, when integrated with clinical workflow and the electronic health record, increases screening of children for ASD, but follow-up by physicians is still flawed. Automation of the subsequent workup is still needed

Committed to Safety: Ten Case Studies on Reducing Harm to Patients

Presents case studies of healthcare organizations, clinical teams, and learning collaborations to illustrate successful innovations for improving patient safety nationwide. Includes actions taken, results achieved, lessons learned, and recommendations

Aiming Higher for Health System Performance: A Profile of Seven States That Perform Well on the Commonwealth Fund's 2009 State Scorecard

Identifies policies and practices linked to high performance in six top-ranked states and the most-improved state in 2007-09. Offers insights into improving coverage, prevention and treatment, avoidable hospital use and costs, equity, and healthy lives

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation

States' Roles in Shaping High Performance Health Systems

Analyzes results from the State Health Policies Aimed at Promoting Excellent Systems survey and a review of current research on efforts to improve state healthcare systems, with a focus on coverage; quality, safety, and value; and infrastructure

Special Libraries, December 1975

Volume 66, Issue 12https://scholarworks.sjsu.edu/sla_sl_1975/1009/thumbnail.jp

Acquisitions Unit Annual Report 2010-2011

Annual report of the Acquisitions Unit, University Libraries, University of Rhode Island for FY11. Covers personnel, work flow, special projects, serial and monograph acquisitions, and goals for the coming year. Supplemental files include data on library materials expenditures by a number of variables as well as important working documents from the year, e.g. serial titles withdrawn, subscription changes for 2011, collection comparisons with other HELIN libraries, fiscal close reports, and a list of e-resources acquired through the HELIN Consortium

RegTech in public and private sectors: the nexus between data, technology and regulation

Higher regulatory compliance requirements, fast and continuous changes in regulations and high digital dynamics in the financial markets are powering RegTech (regulatory technology), defined as technology-enabled innovation applied to the world of regulation, compliance, risk management, reporting and supervision. This work builds on a systematic literature review and a bibliometric analysis of the literature on RegTech, its influential papers and authors, its main areas of research, its past and its future. The resulting multi-dimensional framework bridges across four main dimensions, starting with regulation and technology, where one or more regulations, not necessarily financial ones, are addressed with the support of technologies (e.g. artificial intelligence, DLT, blockchain, smart contracts, API). Data play a central role, as sharing them enables data ecosystems, where additional value can be attained by each market participant, while data automation and machine-readable regulations empower regulators to pull data directly from the banks' systems and combine these data with data obtained directly from customers or other external sources. Several applications emerge, both for regulated entities, covering matters of compliance, monitoring, risk management, reporting and operations, as well as for authorities, which can leverage on RegTech (SupTech) solutions to make policies, to undertake their authorising, supervising and enforcement operations, for monitoring and controlling purposes, and even to issue fines automatically. As a consequence, stakeholders can reap a series of benefits, such as higher efficiency and effectiveness, accuracy, transparency and lower compliance costs but also risks, such as cyber risk, algorithmic biases, and dehumanization

A Systematic Approach to Manage Clinical Deterioration on Inpatient Units in the Health Care System

The transformation of health care delivery in the United States is accelerating at unbelievable speed. The acceleration is a result of many variables including health care reform as well as the covariation occurring with adjustments in regulations related to resident work hours. The evolving care delivery model has exposed a vulnerability of the health system, specifically in academic medical centers of the United States. Academic medical centers have established a care delivery model grounded and predicated in resident presence and performance. With changes in resident work expectations and reduced time spent in hospitals, an urgent need exists to evaluate and recreate a model of care that produces quality outcomes in an efficient, service driven organization. One potential care model that would stabilize organizations is infusion of APNs with the expanded skills and knowledge to instill practice continuity in the critical care environment. A Medicare demonstration project is proposed for funding an APN expanded role and alteration in the care delivery model. Formative and summative evaluation and impact of such an expanded practice role is included in the proposed project. An evolved partnership between the advanced practice nurse and physician will serve to fill some of the gap currently existing in the delivery system of today. As the complexity and acuity of the patients in the hospital escalates, innovation is demanded to ensure a care model that will foster achievement of the quality outcomes expected and deserved