Articles

What Could the Next Generation of Medicaid HCBS Look Like? Funding, Workforce, Quality and System Redesign
The next generation of Medicaid Home- and Community-Based Services will depend on more than expanding access or introducing new payment models. This flagship analysis examines how federal policy, state administration, managed care, provider networks, workforce investment, person-centered outcomes and digital infrastructure could reshape HCBS, and what states, health plans and community providers need to demonstrate for reform to deliver sustainable improvements. Read more...
AI-Powered Risk Detection in U.S. Community-Based Care: From Early Warning to Accountable Human Action
Artificial intelligence could help U.S. community-based care organizations detect emerging safety, continuity and quality risks earlier by connecting signals that conventional monitoring often examines separately. This flagship analysis explores how providers, Medicaid agencies and health plans can use AI-supported risk detection responsibly while protecting rights, strengthening data governance, retaining human judgment and ensuring that prediction leads to proportionate, accountable action. Read more...
The Future of Market Shaping Through Predictive Analytics
Community-based care markets are often managed through evidence of what has already happened. This flagship analysis examines how predictive analytics could help Medicaid agencies, health plans and provider systems anticipate emerging capacity gaps, workforce pressures and changing population needs, then shape more resilient, equitable and responsive provider markets without allowing algorithms to determine individual access or provider opportunity. Read more...
Outcome Prediction Models in Adult Social Care Commissioning
Outcome prediction could change how U.S. community-based care systems evaluate services, allocate resources and identify where better outcomes may be achievable. This flagship analysis examines how Medicaid agencies, managed care organizations and providers can combine longitudinal, operational and person-reported evidence to forecast outcomes while protecting choice, equity and due process and avoiding the misuse of predictive models as automated eligibility or funding decisions. Read more...
Can Social Care Funding Become More Preventive Through Data Intelligence?
Social care and community-based funding often responds after demand, instability or crisis has already become visible. This flagship analysis examines how U.S. Medicaid agencies, managed care organizations, states and providers could use data intelligence to identify emerging need earlier, direct investment toward prevention and connect funding decisions with access, workforce capacity, equity, quality and measurable long-term outcomes. Read more...
Predictive Commissioning and the Future of Public Service Planning in U.S. Community-Based Care
Public service planning is moving beyond retrospective demand and expenditure toward earlier intelligence about need, capacity and system pressure. This flagship analysis examines how U.S. Medicaid agencies, managed care organizations and human services systems can use predictive commissioning to connect population need, provider capacity, workforce, funding, quality and equity while retaining accountable human judgment. Read more...
The Future of Intelligent Commissioning: How Data Could Transform U.S. Community-Based Care
Commissioning decisions shape who receives support, which providers can sustain services and whether community-based systems can respond before capacity fails. This flagship analysis examines how U.S. Medicaid agencies, managed care organizations and other purchasers can use connected workforce, quality, access, utilization, provider and participant evidence to move toward more intelligent purchasing and oversight across HCBS, LTSS, IDD and behavioral health services. Read more...
Predicting Workforce Risk in Supported Living and Home-Based Care Before Service Stability Deteriorates
Workforce instability rarely appears without warning. Across U.S. supported living, HCBS, IDD and home-based care, changes in vacancies, overtime, scheduling, supervision, competency and continuity can reveal emerging risk before services fail. This article examines how providers, Medicaid agencies and managed care organizations can combine workforce intelligence, governance and predictive analytics to intervene earlier while protecting workers, participant choice and service quality. Read more...
The Digital Social Care Workforce of 2035
By 2035, the U.S. community-based care workforce is likely to combine human support, digital systems, AI-enabled decision support, remote care, automation and new specialist roles. This flagship analysis examines how Medicaid, state administration, provider economics, workforce redesign, governance and people’s rights will determine whether digital transformation strengthens HCBS and LTSS rather than simply adding technology to an already pressured workforce. Read more...
Using Workforce Data to Predict Recruitment Gaps Before They Become Care Crises
Recruitment shortages rarely emerge without warning. This article examines how U.S. HCBS, LTSS, IDD, behavioral health and community-based providers can combine workforce, service, financial and quality data to identify capacity risks earlier, target recruitment more precisely and protect continuity. It also explores state and payer responsibilities, data limitations, workforce equity, governance and the appropriate use of predictive analytics and AI. Read more...
Can Digital Workforce Models Improve Continuity of Care Across U.S. Community-Based Services?
Digital workforce models can help community-based providers stabilize scheduling, preserve staff knowledge, strengthen supervision and respond earlier when continuity is at risk. This article examines how technology-enabled workforce design can support HCBS, LTSS, IDD, aging and behavioral health services without replacing relationships, weakening accountability or allowing efficiency goals to override people’s choices and rights. Read more...
Using Predictive Workforce Analytics to Reduce Turnover Across HCBS, LTSS and Human Services
Predictive workforce analytics can help HCBS, LTSS, IDD and behavioral health providers identify emerging turnover risk before experienced employees leave, schedules destabilize or service quality declines. This pillar article explains how providers can combine workforce, scheduling, supervision, wellbeing, quality and operational data to strengthen retention, reduce avoidable recruitment costs and target earlier interventions where workforce pressure is building. Read more...