Schizophrenia – time to commit to policy change

Schizophrenia is recognised as one of the most complex and profound mental health conditions, steeped in both myth and reality. Efforts needs to be multifaceted, including policy development, treatment guidance and scientific innovation, with all stakeholders working together to ensure meaningful progress. This report delves into the unique needs of people with schizophrenia, exploring supportive measures for their well-being, practical and attainable recommendations for change. The message to all nations, policy makers, payers and healthcare professionals is clear: strive for excellence, but most importantly – start somewhere

Impact of Terminology Mapping on Population Health Cohorts IMPaCt

Background and Objectives: The population health care delivery model uses phenotype algorithms in the electronic health record (EHR) system to identify patient cohorts targeted for clinical interventions such as laboratory tests, and procedures. The standard terminology used to identify disease cohorts may contribute to significant variation in error rates for patient inclusion or exclusion. The United States requires EHR systems to support two diagnosis terminologies, the International Classification of Disease (ICD) and the Systematized Nomenclature of Medicine (SNOMED). Terminology mapping enables the retrieval of diagnosis data using either terminology. There are no standards of practice by which to evaluate and report the operational characteristics of ICD and SNOMED value sets used to select patient groups for population health interventions. Establishing a best practice for terminology selection is a step forward in ensuring that the right patients receive the right intervention at the right time. The research question is, “How does the diagnosis retrieval terminology (ICD vs SNOMED) and terminology map maintenance impact population health cohorts?” Aim 1 and 2 explore this question, and Aim 3 informs practice and policy for population health programs. Methods Aim 1: Quantify impact of terminology choice (ICD vs SNOMED) ICD and SNOMED phenotype algorithms for diabetes, chronic kidney disease (CKD), and heart failure were developed using matched sets of codes from the Value Set Authority Center. The performance of the diagnosis-only phenotypes was compared to published reference standard that included diagnosis codes, laboratory results, procedures, and medications. Aim 2: Measure terminology maintenance impact on SNOMED cohorts For each disease state, the performance of a single SNOMED algorithm before and after terminology updates was evaluated in comparison to a reference standard to identify and quantify cohort changes introduced by terminology maintenance. Aim 3: Recommend methods for improving population health interventions The socio-technical model for studying health information technology was used to inform best practice for the use of population health interventions. Results Aim 1: ICD-10 value sets had better sensitivity than SNOMED for diabetes (.829, .662) and CKD (.242, .225) (N=201,713, p Aim 2: Following terminology maintenance the SNOMED algorithm for diabetes increased in sensitivity from (.662 to .683 (p Aim 3: Based on observed social and technical challenges to population health programs, including and in addition to the development and measurement of phenotypes, a practical method was proposed for population health intervention development and reporting

Digital health technologies in the psychosocial treatment of core symptoms of psychotic disorders - literature review and practical aspects

Peer reviewe

Utilization of a Concurrent Query Form to Improve Clinical Documentation in a VA Facility for Patients With Stroke or TIA

Caring for stroke patients diagnosed with acute ischemic stroke (AIS) and transient ischemic attack (TIA) at Veterans Health Administration (VHA) acute care hospitals is a very complex process that centers on accurate documentation. Inaccurate or missing documentation leads to patient safety issues, lower quality care, and inaccurate Veteran Equitable Resource Allocation (VERA) classification for reimbursement. This pilot project’s 3 problems of interest include improving provider response to clinical queries about documentation, capturing national metrics collected by the VHA, and accurately representing veterans in VERA classification. Based on a review of the literature available on patient treatment file (PTF) accuracy and clinical documentation improvement, the researcher used a three-pronged intervention for data collection and management plan. The data were abstracted from 97 (N = 97) AIS and TIA patient treatment files from calendar years 2015 to 2019, then compared with prospective data collected for a period of 3 months, and analyzed for statistical and clinical significance. The results of this pilot project included an increase in provider response to queries, captured metrics, and VERA classification of veterans that satisfies clinical documentation integrity according to VHA directives. Keywords: RN-led CDI program, clinical documentation improvement specialist, clinical and financial CDI outcomes, clinical documentation improvement model

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial

Individuals who access at-risk mental state (ARMS) services often have unusual sensory experiences and levels of distress that lead them to seek help. The Managing Unusual Sensory Experiences (MUSE) treatment is a brief symptom targeted intervention that draws on psychological explanations to help account for unusual experiences. Practitioners use formulation and behavioural experiments to support individuals to make sense of their experiences and enhance coping strategies. The primary objective of this feasibility trial is to resolve key uncertainties before a definitive trial and inform parameters of a future fully powered trial