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deaths from cardio vascular disease, 1 million newborn deaths, 900 000 deaths from tubercu losis, and half of all maternal deaths each year.Quality of care will become an even larger driver of population health as utilisation of health systems increases and as the burden of disease shifts to more complex conditions.The high mortality rates in LMICs for treatable causes, such as injuries and surgical con ditions, maternal and newborn complications, cardio vascular disease, and vaccine preventable diseases, illustrate the breadth and depth of the healthcare quality challenge.Poor-quality care can lead to other adverse outcomes, including un necessary health-related suffering, persistent symptoms, loss of function, and a lack of trust and confidence in health systems.Waste of resources and catastrophic expenditures are economic side effects of poor-quality health systems.As a result of this, only one-quarter of people in LMICs believe that their health systems work well. Health systems should measure and report what matters most to people, such as competent care, user experience, health outcomes, and confidence in the systemMeasurement is key to accountability and improvement, but available measures do not capture many of the processes and outcomes that matter most to people.At the same time, data systems generate many metrics that produce inadequate insight at a substantial cost in funds and health workers' time.For example, although inputs such as medicines and equipment are commonly counted in surveys, these are weakly related to the quality of care that people receive.Indicators such as proportion of births with skilled attendants do not reflect quality of childbirth care and might lead to false complacency about progress in maternal and newborn health.This Commission calls for fewer, but better, measures of health system quality to be generated and used at national and subnational levels.Countries should report health system performance to the public annually by use of a dashboard of key metrics (eg, health outcomes, people's confidence in the system, system competence, and user experience) along with measures of financial protection and equity.Robust vital registries and trust worthy routine health information systems are prerequi sites for good performance assessment.Countries need agile new surveys and real-time measures of health facilities and populations that reflect the health systems of today and not those of the past.To generate and interpret data, countries need to invest in national institutions and professionals with strong quantitative and analytical skills.Global develop ment partners can support the generation and testing of public goods for health system measurement (civil and vital registries, routine data systems, and routine health system surveys) and promote national and regional institutions and the training and mentoring of scientists. New research is crucial for the transformation of low-quality health systems to high-quality onesData on care quality in LMICs do not reflect the current disease burden.In many of these countries, we know little about quality of care for respiratory diseases, cancer, mental health, injuries, and surgery, as well as the care of adolescents and elderly people.There are vast blind spots in areas such as user experience, system competence, confidence in the system, and the wellbeing of people, including patient-reported outcomes.Measuring the quality of the health system as a whole and across the care continuum is essential, but not done.Filling in these gaps will require not only better routine health information systems for monitoring, but also new research, as proposed in the research agenda of this Commission.For example, research will be needed to rigorously evaluate the effects and costs of recommended improvement approaches on health, patient experience, and financial protection.Implementation science studies can help discern the contextual factors that promote or hinder reform.New data collection and research should be explicitly designed to build national and regional research capacity. Improving quality of care will require system-wide actionTo address the scale and range of quality deficits we documented in this Commission, reforming the foundations of the health system is required.Because health systems are complex adaptive systems that function at multiple interconnected levels, fixes at the micro-level (ie, health-care provider or clinic) alone are unlikely to alter the underlying performance of the whole system.However, we found that interventions aimed at changing provider behaviour dominate the improvement field, even though many of these interventions have a modest effect on provider performance and are difficult to scale and sustain over time.Achieving high-quality health systems requires expanding the space for improvement to structural reforms that act on the foundations of the system.This Commission endorses four universal actions to raise quality across the health system.First, health system leaders need to govern for quality by adopting a shared vision of quality care, a clear quality strategy, strong regulation, and continuous learning.Ministries of health cannot accomplish this alone and need to partner with the private sector, civil society, and sectors outside of health care, such as education, infrastructure, communication, and transport.Second, countries should redesign service delivery to maximise health outcomes rather than geographical access to services alone.Primary care could tackle a greater range of low-acuity conditions, whereas hospitals or specialised health centres should provide care for conditions, such as births, that need advanced clinical expertise or have the risk of unexpected complications.Third, countries should transform the health workforce by adopting competency-based clinical education, introducing training in ethics and respectful care, and better supportingThe Lancet Global Health Commission and respecting all workers to deliver the best care possible.Fourth, governments and civil society should ignite demand for quality in the population to empower people to hold systems accountable and actively seek high-quality care.Additional targeted actions in areas such as health financing, management, district-level learning, and others can complement these efforts.What works in one setting might not work elsewhere, and improvement efforts should be adapted for local context and monitored.Funders should align their support with system-wide strategies rather than contribute to the proliferation of micro-level efforts.In this Commission, we assert that providing health services without guaranteeing a minimum level of quality is ineffective, wasteful, and unethical.Moving to a highquality health system-one that improves health and generates confidence and economic benefits-is primarily a political, not technical, decision.National governments need to invest in high-quality health systems for their own people and make such systems accountable to people through legislation, education about rights, regulation, transparency, and greater public participation.Countries will know that they are on the way towards a high-quality, accountable health system when health workers and policymakers choose to receive health care in their own public institutions.

BACKGROUND: Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. OBJECTIVE: Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. METHODS: The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs-video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing-using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. RESULTS: The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. CONCLUSIONS: Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.

Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.

BACKGROUND: The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. OBJECTIVE: This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies. METHODS: A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine "Google Scholar," and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate. RESULTS: Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported. CONCLUSIONS: Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42019141219; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141219.

The SOC-8 guidelines are intended to be flexible to meet the diverse health care needs of TGD people globally. While adaptable, they offer standards for promoting optimal health care and guidance for the treatment of people experiencing gender incongruence. As in all previous versions of the SOC, the criteria set forth in this document for gender-affirming medical interventions are clinical guidelines; individual health care professionals and programs may modify these in consultation with the TGD person.

We describe different models of community care for persons with severe mental illness and review the research literature on case management, including the results of 75 studies. Most research has been conducted on the assertive community treatment (ACT) or intensive case management (ICM) models. Controlled research on ACT and ICM indicates that these models reduce time in the hospital and improve housing stability, especially among patients who are high service users. ACT and ICM appear to have moderate effects on improving symptomatology and quality of life. Most studies suggest little effect of ACT and ICM on social functioning, arrests and time spent in jail, or vocational functioning. Studies on reducing or withdrawing ACT or ICM services suggest some deterioration in gains. Research on other models of community care is inconclusive. We discuss the implications of the findings in terms of the need for specialization of ACT or ICM teams to address social and vocational functioning and substance abuse. We suggest directions for future research on models of community care, including evaluating implementation fidelity, exploring patient predictors of improvement, and evaluating the role of the helping alliance in mediating outcome.