The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care.We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings.The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs.Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations  and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings. The purpose of our paper is to illustrate the application of an evidence-based, structured methodology for identifying, prioritizing, and (where appropriate) suggesting new measures of health care quality, using primary health care as a case example. Primary health care provides the principal point of entry for patients into the health care system in America and many other countries, and is often the gateway for initially accessing most other specialties. Research has consistently demonstrated that people in countries and health systems with high-quality primary care exhibit better health outcomes and greater health equity . WithoutBecause of its central role in the workings of the health care system and health of the public, a high-performing, high-quality primary care system is essential; private and public entities alike have devoted considerable resources toward that goal, making it an ideal setting for demonstrating the application of this methodology. For our purposes, we conceptualize primary care as defined by the latest report from the (United States) National Academies of Science Engineering Medicine (NASEM) on implementing high-quality primary care:the provision of whole-person, integrated, accessible, and equitable health care by interprofessional teams that are accountable for addressing the majority of an individual’s health and wellness needs across settings and through sustained relationships with patients, families, and communities.1This latest definition is consistent with and builds upon the work of other public and private organizations, such as the United Kingdom’s National Health Service (NHS) , the WorAs reflected by its modern conceptualization, primary care delivery has evolved over the last few decades, facilitated by significant advances in medicine, technology, and policy. Consequently, measurement frameworks are needed to assess the quality of the infrastructure, workforce configurations, and processes available in primary care practices due to the complexity of primary care. Health-care systems in several countries have embarked on creating such frameworks  for primSuch proliferation of performance measures, of both high and low quality, bears unintended consequences on the primary care workforce; for example, in 2014, Kansagara and colleagues  further Despite advances in primary care delivery in the last several decades , the fundamental objectives of primary care  and the nature of the work typically performed in primary care  have remained largely unchanged –20). GivIdeally, scientific evidence informs the development, validation, and ultimately the application(s) of performance measures. Specifically, good performance measures propose criteria by which the performer is to be evaluated. A set of performance measures, however, can serve multiple purposes, including assessment of similar criteria across different settings  and different people . Further, performance measures are adaptable in that they can serve more than one purpose or interpretation of their criteria. For example, blood pressure and heart rate measures  can indicate an individuals’ cardiovascular health . HoweverThe Productivity Measurement and Enhancement System (ProMES) is a structured, evidence-based, comprehensive productivity improvement approach; originating from the industrial/organizational psychology and management literatures, it is firmly grounded in motivational theory, the science of collective sensemaking, and performance measurement –30. ProMPrevious research in intensive care unit and mental health settings have shown successful use of ProMES to improve efficiency and quality of care. For example, in studies conducted with a German mental health hospital, the ProMES methodology was used across organizational levels: the top management team, samples of nurses, and three samples of technicians. As a whole, results indicated that overall productivity scores for participating units were .78 standard deviations higher, on average, after implementation . More reOne unique feature of ProMES is that the resulting measures include non-linear functions that explicitly tie performance levels on a given measure to its contribution to the organization’s values; in this way, the application of ProMES yields a more nuanced approach to prioritizing measures than simple linear weights, while allowing direct comparison(s) between measure(s) . In shorOur objective in this paper is to illustrate the application of ProMES to select and curate a targeted set of primary care quality measures, prioritized according to their overall contribution and value to primary care. Although the illustrative case below is part of a larger project  where prThis study was reviewed and approved by the Baylor College of Medicine Institutional Review Board (IRB), protocol #H-42358. Human subjects consent was waived by the IRB, as no human subjects were involved: the participants described below were deemed subject-matter experts, not subjects of the study.The first step in the ProMES approach was the selection of our subject matter expert (SME) panel, known in ProMES parlance as a design team. We invited SMEs who met the following criteria: (1) filled gaps in the project team’s knowledge base, (2) possessed significant experience or a leadership role in primary care settings, and (3) were highly knowledgeable of and experienced with performance measures and/or electronic health record (EHR)-sourced data. To ensure representativeness in the design team, we additionally ensured the team consisted of experts from both inside and outside the VHA  and represented multiple stakeholder groups including active primary care clinicians, administrators, performance measure data managers, and measurement experts. Our final team consisted of nine subject matter experts in addition to our project team. generated through ProMES.We adapted the ProMES steps involved in developing a performance measurement system to accomplish our objectives. Traditional ProMES methodology involves forming a design team of SMEs, developing objectives, generating performance indicators, and generating assessments of value for each indicator . Each step is described below. For this project, we adapted the indicator development process (Step 2) so that our design team selected from existing measures, rather than generating new ones. We identified potential indicators from an existing pool of primary care measures available on a national level through the VHA. Each potential indicator/existing measure was thoroughly vetted for quality against ProMES indicator quality criteria in order to mirror the methodologically rigorous characteristics of performance indicators The design team consisted of nine subject matter experts in addition to our study team, selected as described in the Participants section, above.A fundamental assumption of ProMES is that effective performance measurement cannot be accomplished without knowing what overarching performance objective is sought. Our design team was led by project team facilitators in a 1-day workshop to identify the clinical objectives for primary care. According to ProMES, objectives are defined as the essential things a unit  does to add value to the organization; in other words, objectives are the main result (and associated characteristics) of the primary care team’s work . Examples of health care objectives include patient care performed according to quality standards, effective patient throughput, and personnel allocation matched to patient workload. The key question the design team was charged with answering was “what is the primary care team trying to accomplish when it delivers care?” The facilitators guided the design team to arrive at three to six objectives that met the following criteria: (1) was stated in clear terms, (2) designed so that if exactly that objective was accomplished, the facility (and thus the patient) would benefit; (3) the set of objectives cover all important aspects of clinical care; (4) was consistent with broader objectives ; and (5) leadership was committed to each objective. Objectives were compared against these criteria to ensure their utility in facilitating the subsequent steps of the process.de novo, rather than selected from a set. However, as such an abundance of measures already exist for primary care, the ProMES process was used slightly differently, yet innovatively–to cull, rather than generate, indicators of performance. For each objective, the design team answered the following question: “How would you show that the primary care team is meeting the stated objective?” To accomplish this, the design team received a document containing names, sources, and operational definitions of all outpatient clinical performance measures currently tracked by the VHA that were available at the team level. To ensure generalizability beyond the VHA health care system, any measures that captured VHA-specific processes or policies  were excluded. Measures for the design team’s consideration were drawn from VHA’s principal outpatient clinical data sources and reports: VHA’s Corporate Data Warehouse, the principal repository of clinical and administrative data; the Strategic Analytics for Improvement and Learning (SAIL) system, VHA’s system for summarizing hospital performance, which includes the HEDIS measures used by most medical practices nationwide; and the Electronic Quality Measures (eQM), which provides, real-time, 100% sampling of patients for measure calculation.Having identified primary care teams’ healthcare delivery objectives, the next step was to identify indicators for these objectives. In the classic ProMES process, these indicators would be generated The design team selected indicators via a two-step process. Design team members received documents describing the candidate measures two weeks in advance of the aforementioned day-long workshop so they would have adequate time for review. As part of the day-long workshop, the facilitators led the design team through a preliminary group voting process to identify the strongest candidate measures for each objective. In the weeks following the workshop, the team met virtually in a series of weekly 1 to 1½-hour facilitator-led meetings for up to a total of approximately 12 hours of meeting time. For each objective, the design team reviewed each candidate measure that survived the preliminary voting process. The team collectively narrowed down the list to a targeted set of 4–6 performance indicators per objective that captured the extent to which the primary care objectives were being achieved. Each indicator needed to meet multiple criteria regarding its validity, comprehensiveness, impact, feasibility, and usability.Indicators provide information about what is valued in performance  order and a scheduled colonoscopy signals timeliness in care delivery); however, what level of performance is acceptable, or how much a given level of improvement is valued (Is an average of 7 days acceptable? How much worse is 8 days? 10? How much better is 5 days?) is provided by a ProMES tool called contingencies. One of the most novel aspects of ProMES is the use of contingencies. Contingency development generates a function for each indicator that shows how much the different amounts of the indicator  contribute to overall effectiveness. By relating each indicator to overall effectiveness, they are put on the same measurement scale, which ranges from -100 to +100. Thus, the various indicators can be directly compared, prioritized, and combined into a single measure if needed. Most importantly, it reflects an explicit statement of what elements of primary care are valued, and what level of primary care-related performance is expected and valued by the team and the facility.Once the design team selected the indicators for each of the objectives, the classic ProMES approach was followed  to develop a contingency function for each indicator. In doing so, members of the design team identified the maximum and minimum possible levels of performance, the minimum expected performance level, and scaled the various levels of performance to a common measure of effectiveness for each indicator. This was accomplished with the aid of the principal investigator serving as facilitator who, for each indicator, elicited discussion about each of the aforementioned data points, and guided each discussion to consensus through real-time, iterative polling of the design team.The design team identified three fundamental objectives for delivery of high-quality primary care:Objective 1. Ensure patient has appropriate access to preventive, acute, or chronic health care services when needed.Objective 2. Build a trusting, effective, sustained partnership between the health-care team, the patient, and his/her caregiver(s) towards shared goals.Objective 3. Deliver safe and effective care that comprehensively addresses a given patient’s particular ecological, biological, and/or psychosocial needs.A fourth objective, proactively monitor and engage with patient population(s) to optimize their treatment plans and health behaviors towards improved overall health, was originally identified. However, as the design team selected performance indicators for each of the objectives, it became evident that the fourth objective was subsumed under the other three objectives. Thus, the design team opted for a more succinct set of objectives and instead allowed for a slightly greater number of indicators in total, to capture this aspect of the primary care performance domain.The design team selected sixteen performance indicators from the 44 pre-vetted measures that already exist in three different data sources for primary care, as outlined in Step 3. One indicator, Team 2-Day Post-Discharge Contact Ratio, was selected as an indicator for both Objective 2 and 3. In addition, contingency curves were created for each of the indicators using the contingency functions developed by the design team as described in Step 4. The ProMES process is meant to guide measurement development or selection that yields a concise yet comprehensive list of indicators that contribute to an organization’s overall effectiveness ,35. The Using primary healthcare as an illustrative example, thist paper details the application of an evidence-based approach to select a curated set of healthcare quality indicators, prioritized according to their overall contribution and value to healthcare organizations. In our primary care example, our, o design team of nationally recognized SMEs collectively identified three primary care delivery objectives, selected 16 currently existing indicators of primary care quality directly tied to these objectives, and called for 13 additional indicators requiring further development to address current gaps in the primary care performance measurement domain. The indicators were selected independent of clinic size or configuration, so that clinics of many configurations  could benefit from their use.Results speak to the type of indicators readily available for each stated objective of care. Our first care objective, centered around access to care, highlights the overall importance of primary care facilities to maintain sufficient capacity. In robustly assessing this care objective, indicator(s) for this objective also capture(s) insufficient or ineffective access to primary care by capturing patients’ visit(s) to the emergency department instead of their primary care clinic for their care. Interestingly, our second care objective, covering the importance of maintaining a trusting partnership with the patient, contained relatively few existing indicators, primarily due to the challenges inherent in self-report data and lack of available objective measures for processes of care delivery face to face . Finally, our third objective of achieving technical quality contains an abundance of measures, reflecting the clinical, condition-specific origins of the performance measurement movement in health care. Measures for these objectives revolve around evidence-based processes for the treatment of highly prevalent chronic physical and mental conditions most commonly managed in the primary care setting; similar to the access objective, an additional facet captures ineffective technical quality through hospitalizations resulting from ambulatory care sensitive conditions and hospital admissions. In turn, our study revealed the existing discrepancies between ideal and current measurement of care quality. Specifically, although assessing the quality of less tangible but essential activities such as disease education and shared decision-making in the context of relationship building with patients would ideally form a significant proportion of quality care measurement, a disproportionate number of existing measures focused on the more readily measured technical competencies in clinical practice (Objective 3). A balanced number of indicators across all three objectives would comprise a more comprehensive set of care quality measures.Our illustrated adaptation of ProMES to quality improvement in healthcare carries both scientific and practical implications. Scientifically, our paper adapts a structured approach from outside health care to provide a structured means of selecting and prioritizing healthcare quality indicators, thus expanding the existing health-care literature addressing quality indicator frameworks and development methods. For example, prior research has advanced the application of several frameworks to categorize clinical indicators, including clinical categories , proposePractically, performance measures selected and prioritized by their value in primary care using our modifications to ProMES help clarify where to best focus efforts, making implementation of targeted care quality improvement initiatives more effective. For example, by deriving high-value metrics, organized by objective with quantifiable prioritization, we anticipate the set of indicators identified in our case study could apply to a diverse set of stakeholders, including but not limited to policy-makers, primary care clinicians, and administrators in healthcare organizations. Furthermore, as more primary care settings transition toward a team-based delivery approach, we believe our curated set of indicators provide more meaningful, appropriate, and high-quality assessments of primary care team effectiveness, enabling low-resource settings  to improve the care they deliver .In addition to the viability of the process demonstrated herein, the measures identified by the SMEs bear implications to the scientific and primary care clinical and scientific communities. Practically, our proposed measure set establishes precedent for communicating policy explicitly within care quality measurements. Namely, in regard to care quality among primary care clinics, our targeted manner of measurement explicitly prioritizes the value of each care measure in their overall contribution and value to primary care. In practice, results of using these measures could translate to greater actionability for quality improvement initiatives in the primary care setting. Further, the proposed measures could be adopted to evaluate clinics who wish to pursue certain types of certification, such as PCMH certification through the Joint Commission. Based on currently available data collection techniques, the set of measures presented in our findings offers a less obtrusive, and thus less resource-intensive manner by which team-based primary care quality can be ascertained in that it relies on existing sources of quality data and methods of data capture.Theoretically, our measures and their respective objectives provide clear domains for measurement of primary care quality that are consistent with modern conceptualizations of primary health care, and which could lead to quality improvement interventions that are better aligned with the goals and the promise of primary care.Our resulting set of measures is necessarily limited by what was available in existing VHA data sources; indeed, such limits were observed in the need to propose new measures (not presented here) that did not exist in the current data source in order to comprehensively cover the primary care performance domain. Thus, in the current measure set, outcome measures are overrepresented relative to process measures, whereas the reverse is true in the proposed measures.Although leveraging available measures avoids several unintended consequences involved in care quality measure creation, existing data sources have several documented problems. First, use of electronic health record data for research purposes is often questioned due to completeness and quality of data entered. Second, sampling strategies used to collect more subjective measures of care quality  collect data from a sub-set of a clinical teams’ patient panel and may not fully represent the team’s provision of care. Although no data source is perfect, we hope to target efforts on improving the quality of health systems by building upon existing techniques rather than inventing new methods of data capture. Furthermore, not all practices or systems have the infrastructure needed to measure, capture, and utilize the data to measure care quality. Some practices or systems may not have the resources or have competing resource demands, such as needing to invest resources in equipment or buildings rather than performance measurement. Funding initiatives for practices that want to adopt a PCMH model should provide sufficient incentives to significantly reform infrastructure including a measurement system to measure care quality.A core assumption of this paper is that the primary care performance objectives result in patients’ improved health and that they align with patients’ goals. Moreover, we are mindful of patient centered issues and needs that are not captured by the objectives, such as the healing comfort a healthcare professional can provide. Future efforts should more explicitly capture the needs of the patient when identifying clinical performance objectives and indicators, as exemplified in several of the measures suggested by the SMEs.Finally, psychometric procedures have not been conducted on the selected measures as a set to confirm statistically that the selected indicators indeed load onto their respective objectives. Obviously, this check cannot be performed on the proposed measures that do not, as of yet, exist.We conclude that through the use of an evidence-based approach to performance measure development, a targeted set of performance measures can be curated to track progress toward specific objectives in delivering high-quality primary care. The measures identified by the SMEs provide an actionable catalogue that can serve as a first step toward interoperability of electronic health record systems. Future work toward this goal should address both logistical considerations  and lingering measurement challenges, such as the best way to operationalize these measures for teams working in complex and shifting situations .S1 File(DOCX)Click here for additional data file.


Early adversity is associated with compromised health and well-being in later life, but whether social functioning mediate the association is unclear. We examined 2 longitudinal samples of older adults (>= 50 years) whose baseline surveys were between 2006 and 2008 with follow-up until 2016 in the Health and Retirement Study  and its sister study in England . Health outcomes included depressive symptoms, chronic health conditions, and subjective memory complaints. Social relationships were measured by contacts, relationship strains, and feelings of loneliness. Early adversity was measured by parental physical abuse and alcohol and drug problems in the family before the age of 16. Patterns of association were similar in these 2 samples, where social contacts decreased over time, while relationship strains and loneliness increased especially for older adults with early trauma, which in turn mediated the associations between early adversity and poorer later health.


Quality of care is consistently shown to be inadequate in health-care settings in many low-income and middle-income countries, including in private facilities, which are rapidly growing in number but often do not have effective quality stewardship mechanisms. The SafeCare programme aims to address this gap in quality of care, using a standards-based approach adapted to low-resource settings, involving assessments, mentoring, training, and access to loans, to improve clinical quality and facility business performance. We assessed the effect of the SafeCare programme on quality of patient care in faith-based and private for-profit facilities in Tanzania.In this cluster-randomised controlled trial, health facilities were eligible if they were dispensaries, health centres, or hospitals in the faith-based or private for-profit sectors in Tanzania. We randomly assigned facilities (1:1) using computer-generated stratified randomisation to receive the full SafeCare package (intervention) or an assessment only (control). Implementing staff and participants were masked to outcome measurement and the primary outcomes were measured by fieldworkers who had no knowledge of the study group allocation. The primary outcomes were health worker compliance with infection prevention and control (IPC) practices as measured by observation of provider–patient interactions, and correct case management of undercover standardised patients at endline (after a minimum of 18 months). Analyses were by modified intention to treat. The trial is registered with ISRCTN, ISRCTN93644888.Between March 7 and Nov 30, 2016, we enrolled and randomly assigned 237 health facilities to the intervention (n=118) or control (n=119). Nine facilities (seven intervention facilities and two control facilities) closed during the trial and were not included in the analysis. We observed 29 608 IPC indications in 5425 provider–patient interactions between Feb 7 and April 5, 2018. Health facilities received visits from 909 standardised patients between May 3 and June 12, 2018. Intervention facilities had a 4·4 percentage point  higher mean SafeCare standards assessment score at endline than control facilities. However, there was no evidence of a difference in clinical quality between intervention and control groups at endline. Compliance with IPC practices was observed in 8181 (56·9%) of 14 366 indications in intervention facilities and 8336 (54·7%) of 15 242 indications in control facilities . Correct management occurred in 120 (27·0%) of 444 standardised patients in the intervention group and in 136 (29·2%) of 465 in the control group .SafeCare did not improve clinical quality as assessed by compliance with IPC practices and correct case management. The absence of effect on clinical quality could reflect a combination of insufficient intervention intensity, insufficient links between structural quality and care processes, scarcity of resources for quality improvement, and inadequate financial and regulatory incentives for improvement.UK Health Systems Research Initiative . Quality of care has risen high up the global health policy agenda. Together with expanding coverage and financial protection, quality of care is a key component of health reforms inspired by universal health coverage.8Evidence before this studyWe searched for studies English, published up to December, 2019, that evaluated the effect of interventions working with private facilities on clinical quality of care in low-income and middle-income countries (LMICs), using PubMed, Web of Science, and Google Scholar  and websites of key donor agencies, research institutions, private sector-focused consultancies, non-governmental organisations, and academic organisations including Private Health in Developing Countries, The World Bank, US Agency for International Development, Population Services International, Private Sector Partnerships One, Health Systems 20/20, Reproductive Health Vouchers, and the UK Department for International Development (grey literature), using terms related to the domains of “private”, “LMIC”, and a range of interventions . No evaluations of SafeCare or similar models in private facilities were identified. We reviewed controlled evaluations of accreditation and social franchising interventions, which have some similar components to SafeCare: three randomised controlled trials found no or weak efficacy with regard to clinical quality, while three non-randomised studies had mixed effects, and might have been affected by selection bias.Added value of this studyThis study is the first evaluation of the effect of SafeCare on clinical quality of care, and one of very few randomised controlled trials on any intervention aiming to improve quality in the private sector in LMICs. The SafeCare intervention led to a small improvement in adherence to SafeCare standards, but there was no effect on clinical quality of care.Implications of all the available evidenceSafeCare programme can improve structural quality, but there is no evidence that, at the intensity usually implemented, it has an effect on clinical quality of care. Improving clinical quality of care remains an important challenge and the evidence base does not yet provide guidance on which approaches are most promising.The There are concerns regarding quality of care in both the public and private health-care sector.Quality of care is extremely variable across such private health-care facilities,These quality-of-care concerns led to the development of the SafeCare model, an innovative approach that adapts international accreditation standards to low-resource settings and supports health facilities to attain higher standards, recognising improvement through stepwise formal certification.The objective of this study was to evaluate the effectiveness of SafeCare in improving clinical quality of care at the facility level. We chose to evaluate SafeCare in Tanzania, where it was being implemented on a large scale in private facilities across the country and with UK Government funding through the Human Development Innovation Fund. The private sector accounts for 29% of health facilities in Tanzania (15% faith-based and 14% for-profit), including hospitals and primary level facilities .We did a cluster-randomised controlled trial in private health facilities (clusters) in mainland Tanzania. Facilities were screened and recruited by implementing partners of PharmAccess  in Tanzania: the Association of Private Health Facilities in Tanzania (APHFTA), which represents mainly for-profit facilities, and the Christian Social Services Commission (CSSC), which represents mostly faith-based facilities. Facilities were recruited from the Northern, Eastern, Central, Southern, and Southern Highlands zones of Tanzania .The study protocol was approved by the ethics committees of the Ifakara Health Institute , the National Institute of Medical Research , and the London School of Hygiene & Tropical Medicine . Alongside this effectiveness study, we also did a process evaluation to understand implementation, mechanisms of effect, and context,Eligible facilities were dispensaries and health centres that were members of APHFTA, and dispensaries, health centres, and hospitals that were members of CSSC. Facilities were ineligible if they refused to provide consent to participate, provided specific services only , were tertiary hospitals, or had previously been exposed to SafeCare. We worked with APHFTA and CSSC to select a non-random list of 280 potentially eligible facilities for participation in the study . The parHealth workers participating in the study comprised those interviewed for a facility survey, those whose IPC practices were observed, and those visited by standardised patients. For the facility survey and IPC observations, written informed consent was obtained from the health worker before the start of data collection. At the time of the facility survey, facility managers were also asked to consent to visits from undercover standardised patients at an unspecified date within the next 3 months. Patients participating in the study were those directly observed during patient–provider interactions, and those who gave an exit interview before leaving the facility. For the observations, all patients were eligible if they or their adult caretaker gave verbal informed consent. Individuals aged younger than 18 years were excluded if they were not accompanied by an adult caretaker. For the exit interviews, patients were eligible if they were aged at least 18 years or were accompanied by an adult caretaker, had received curative outpatient care , and had completed their visit to the facility (including collecting prescribed treatments and making payments). Written informed consent was obtained from the patient or caretaker before the start of the interview.We randomised facilities (clusters) to the SafeCare package (intervention) or an initial SafeCare assessment with no further support (control) in a ratio of 1:1. Randomisation was stratified by partner organisation, recruitment cohort, hospital or non-hospital, and geographical zone. Specifically, we randomly allocated health facilities to intervention or control, stratifying the sample so that the proportion allocated to each of the two groups was the same within each stratum. Randomisation of facilities was done using a computer-generated random number in Stata version 14.1.A letter revealing the study group assignment was sealed in an envelope labelled with the facility name and given to a SafeCare quality assessor who opened the envelope to inform the facility manager of the allocation once written informed consent was given and the baseline SafeCare assessment was complete. PharmAccess staff, partner organisations, and facility participants were masked to study group allocation during recruitment and baseline assessment, but not for the rest of the study. They were also masked to outcome measurement—ie, they did not know what clinical quality metrics they were to be assessed on.The SafeCare programme supports private health facilities to improve quality of care and business performance. The programme also includes quality-related policy support and advocacy at the health system level, but the trial intervention evaluated only the direct support to facilities. The SafeCare intervention was implemented by partners APHFTA and CSSC. Following initial sensitisation activities, SafeCare assessments were done by the implementation staff in both intervention and control facilities at baseline and, after a minimum of 18 months, at endline. Control facilities received a simple report of their assessment, but no further support. Separate data collection was done by the research team at endline, including observations of IPC practices, standardised patient visits, a survey of facility managers, and patient exit interviews .The initial SafeCare assessment assessed 170 standards grouped into 13 service elements: governance and management, human resource management, patient rights and access to care, management of information, risk management, primary health care, inpatient care, surgery and anaesthesia, laboratory services, diagnostic imaging, medication management, facility management services, and support services. Quality assessors measured how well facilities met the criteria, awarding an overall score between 0 and 100, and a performance level of one to five, with higher scores indicating better results. Quality assessors were employees of PharmAccess and were typically clinicians  who had completed a 70 h training programme with PharmAccess.Medical Credit Fund, which is also part of the PharmAccess Group, for underwritten loans to fund specific quality improvement activities.Facilities in the intervention group received a quality improvement plan that highlighted specific areas for improvement, actions to be taken, and the facility staff member responsible; mentoring visits from quality assessors to monitor progress on implementation of the quality improvement plan; training on topics such as infection control, waste management, customer care, business management, record keeping, and patient rights; and the opportunity to apply to the Quality assessors provided direct support to the managers of facilities in the intervention group, in the form of in-person mentoring visits to facilities, onsite training sessions, and offsite classroom training sessions for groups of facilities, attended by managers and clinicians. A full-time business analyst, employed by the Medical Credit Fund, made facilities aware of the SafeCare service during regional meetings attended by managers, and supported the writing of business cases and loan applications. Mentoring visits were intended to be quarterly (at least five visits were expected to take place in the 18–24-month period between baseline and endline SafeCare assessments), and staff from each facility were expected to attend at least two training sessions (either onsite or in the classroom).We measured health provider compliance with IPC practices using observations of provider–patient interactions. 6 h of observation were done in each facility over the course of 1 day: 3 h in the consultation rooms, 1·5 h in the laboratory, and 1·5 h in the injection or dressing room. Fieldworkers recorded every indication  and action (taken to mitigate the risk) using a tool that we developed according to WHO guidelines and previous studiesStandardised patients were undercover healthy fieldworkers, trained to present at health facilities reporting specific symptoms and medical history, and to record the care they received. We describe the methods and the protocol for standardised patient safety in more detail in the In terms of contamination, no control facilities received a quality improvement plan or mentoring visits, and seven control facilities attended some training. In practice, both intervention and control facilities had the opportunity to apply for a Medical Credit Fund loan, and two control facilities obtained such a loan during the study period.The two prespecified primary outcomes of the trial were observed compliance of health workers with IPC practices and correct case management of standardised patients at 18–24 months. The choice of outcomes reflected close consultation with staff involved in SafeCare's design and implementation, to capture the programme's broad aim of improving clinical quality of care, together with its emphasis on IPC practices.Compliance with IPC practices was defined as the proportion of indications for which a correct action was done.Correct case management was defined as the proportion of standardised patients who were managed in accordance with the national standard treatment guidelinesSecondary outcomes were the facility SafeCare assessment score ; patient experience-of-care score (based on responses to 21 Likert questions in the patient exit interview); patient out-of-pocket spending ; facility caseload per month ; and facility revenue per month (measured by the facility survey using facility records on each income source for the 3 preceding months). The methods for measuring the secondary outcomes are described in more detail in the We planned to recruit 240 health facilities. For compliance with IPC practices, on the basis of estimated compliance of 31% in the control groupThe analysis of the primary outcomes accounted for clustering using a multilevel mixed-effects logistic regression that included facility random effects and stratum fixed effects. A modified intention-to-treat analysis was done for all endpoints, and included all facilities that remained open until the corresponding endline assessment. We report effect sizes as both marginal (absolute) effects and odds ratios. For compliance with IPC practices, analysis was at the level of indication. The primary outcome was a dichotomous outcome that is present if the health worker undertook the correct IPC action corresponding to the indication, as defined in the The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.Facilities were recruited from March 7 to Nov 30, 2016. 280 facilities were selected as potentially eligible. After approaching the facilities, 43 (15%) were ineligible or unwilling to participate, leaving a total of 237 facilities participating, of which 118 were randomly assigned to the intervention group and 119 to the control group . Complia118 (100%) facilities in the intervention group received a quality improvement plan based on a visit from a quality assessor. Fidelity was lower with respect to mentoring visits (mean 3·1 visits [SD 1·5] of five expected) and training sessions . Only two of the 118 intervention facilities received a loan as part of the intervention.There was no difference in the baseline SafeCare score between the two randomised groups . The intIn the intervention group, 8181 (56·9%) of 14 366 indications were met with IPC-practice compliance and in the control group 8336 (54·7%) of 15 242 indications were met with IPC-practice compliance, with an absolute difference of 2·2 percentage points  compared with 50·8% (48·2–53·3) for facilities in the control group . The difWe carried out an exploratory cross-sectional analysis of our own data to assess associations between SafeCare level and clinical quality. Pooling intervention and control facilities, we found a modest positive correlation between SafeCare level and correct standardised patient management , and no association between SafeCare level and IPC-practice compliance (p=0·13).We did a randomised controlled trial of an intervention package designed to address quality of care in a holistic manner in health facilities in a low-resource setting. The intervention started from a very low base in terms of clinical quality; IPC-practice compliance in the control group at endline was observed in only 55% of indications, and correct management occurred in just 29% of standardised patients. The intervention had a modest positive effect (an increase of 4·4 percentage points) on compliance with SafeCare standards, as measured by the SafeCare assessment score. However, this increase in SafeCare score did not translate into an improvement in clinical quality, with 57% of indications being compliant with IPC practices and only 27% of standardised patients receiving correct case management in the intervention group. The findings are perhaps unsurprising given the small effect of the SafeCare package on SafeCare score results.Turning to the strengths and limitations of the evaluation, generalisability was enhanced by the study's large scale, wide geographical reach, inclusion of both faith-based and for-profit facilities, and real-world operational conditions. By design, control facilities received a copy of their baseline SafeCare report, but as the report was not accompanied by any explanation or further support, implementers felt it was very unlikely to have led to a substantial quality improvement in most facilities. A key strength of the evaluation was the use of robust measures of clinical quality of care: IPC observation and standardised patients. Process measures such as these directly assess provider behaviour and adherence to established clinical guidelines, and are therefore much more closely related to health outcomes than structural measures. IPC observations were potentially susceptible to the Hawthorne effect,We did not do a costing of the intervention, but it is possible to roughly estimate the cost per facility on the basis of the overall SafeCare expenditure for Tanzania, of which the intervention group facilities were part. The total grant was for US$3·9 million for 5 years, and 466 facilities were enrolled. Excluding $158 000 not directly related to the facility-level intervention implies a cost of just over $8000 per facility.Although no other evaluations of SafeCare-style programmes were identified in private facilities in LMICs, two evaluations of similar accreditation programmes for private hospitals had similar results to the current study. A randomised controlled trial in South AfricaQuality of care was poor in both intervention and control groups. Less than a third of standardised patients in both groups received the correct care for their condition, with particularly low rates for those presenting with asthma and upper respiratory tract infection . CompliaTo explore the reasons for the absence of improvement on clinical quality, we consider the fidelity and quality of implementation, contextual factors, and the functioning of the expected causal mechanisms.Facility staff were positive about SafeCare. They found the SafeCare standards well conceived and appreciated their holistic nature across the facility, although some smaller facilities perceived the standards as very demanding. The staff praised the clarity of the quality improvement plans, and found implementing staff friendly and helpful, particularly valuing mentorship visits as important for guidance and morale. The main concern of facility staff was the infrequency of mentoring visits, which partly reflected the relatively low intensity of the planned intervention, together with lower than planned implementation: facilities received less than two-thirds of planned mentoring visits and training sessions. Implementation staff explained that a key factor behind lower than expected implementation was delays in receiving their budget from the funder. Implementation staff also highlighted the challenges of reaching some remote facilities, and the reluctance of some facility staff to attend classroom training without compensation. However, this intensity of support is not atypical of SafeCare elsewhere in Africa.There was particularly low uptake of loans to help facilities pay for any substantial upgrading required, obtained by only two intervention facilities, both for-profit, late in the intervention period. Faith-based facilities generally could not take loans because church property could not be used as collateral. Low uptake in for-profit facilities was said to reflect initial problems faced by PharmAccess in establishing relationships with banks, facilities' poor financial records and credit history, and their concerns about high interest rates, the duration of the application process, and the general economic outlook.In terms of contextual factors, many facilities felt that the deterioration in Tanzania's economic situation negatively affected facility incomes, which might have discouraged investment, and CSSC facilities reported that government support to faith-based facilities had been reduced. Both intervention and control facilities were exposed to a range of other quality-related activities during the study period. This included regular supervision from district health teams, quality improvement programmes run by other non-governmental organisations often for specific health areas (73% of facilities reported participating in at least one such programme), and the government's star rating assessments. These assessments were based on a similar but more narrow assessment of structural quality than SafeCare, and also involved a quality improvement plan, but no training or mentoring. Nearly all facilities had a star rating assessment during our study period, and facilities were concerned that a poor star rating assessment could risk facility closure. Empanelment of private facilities within Tanzania's social health insurance mechanism, the National Health Insurance Fund, was also increasing over the study period. Although participation in quality-related programmes was balanced between study groups , it is pThere could also be weaknesses in hypothesised intervention causal mechanisms. The SafeCare approach rests on changes in structural quality (the SafeCare score) leading to changes in care processes. However, evidence has shown that structural quality is often poorly correlated with process quality.The intervention theory also relies on facilities being able to afford to improve standards. Although standards had negligible budget implications, smaller facilities were said to struggle to pay for more qualified staff, or a regular supply of IPC-practice materials. Finally, a key assumption is that facility staff will be motivated to increase compliance with SafeCare standards, either because of intrinsic concern for quality of care, or because they view SafeCare as good business sense. However, the link between SafeCare standards and increased facility income could be tenuous if patients cannot easily perceive quality improvements or if providers do not expect patients to be sensitive to improvements. Stronger external incentives might enhance motivation—eg, if SafeCare scores were directly linked to facility empanelment or reimbursement in social health insurance, or to regulatory penalties.Quality of care was low in both intervention and control facilities, highlighting the importance of developing strategies to improve quality of care at the provider and system levels. However, although the SafeCare intervention led to modest improvements in SafeCare assessment scores, no effect was seen on clinical quality of care. These findings indicate that a higher burden of proof should be placed on policy makers and funders looking to invest in such interventions. Further experimentation and evaluation is clearly needed to identify effective and affordable quality improvement approaches, with possible strategies including increased intervention intensity using digital approaches, more careful selection of facilities for enrolment, creation of stronger financial or regulatory incentives for quality improvement, and increased focus on improving clinical care processes.Data will be available at the London School of Hygiene & Tropical Medicine Data Compass site.Individual participant data that underlie the results reported in this Article , after deidentification, along with the study protocols, data collection tools, and analytic code, will be made available immediately following publication without end date, to anyone who wishes to access the data for any purpose. This study, through the Health Systems Research Initiative, was partly funded by the UK Government's Department for International Development, which also funded the SafeCare intervention through its Human Development Innovation Fund programme.


Project aim:To reduce the use of oral psychotropic PRN* medication on Ward 3 AMHIC  by 20% by May 2020(*PRN = Pro re nata/As required)On Ward 3, we identified a number of unintended negative consequences of PRN medication to both patients and staff.These included issues with over-use, dependence and side effects; as well as loss of staff ownership and challenging interactions with patients, .Following the success of our Child and Adolescent Mental Health Inpatient colleagues in this area, we decided to embark on a project to change practice within our ward.In order to quantify the problem, we first collected baseline data on current use of psychotropic PRN medication.As a multidisciplinary project team, we then brainstormed potential contributory factors and displayed these visually as a driver diagram.Safe prescribingSafe administration,Safety culture.Project measures were also agreed as follows:This divided our project into 3 main areas:Outcome: Number of doses of oral psychotropic PRN medication administered per weekBalancing: Violent incidents; IM administrations of psychotropic medicationProcess: Time taken to complete interventions; Patient and staff satisfaction. Change ideas were selected and implemented sequentially, using Plan-Do-Study- Act methodology.Weekly review of PRN prescribingNursing administration sheetData were collected weekly and plotted on our run chart.These included:By the end of May 2020, we had exceeded our initial goal, reducing the weekly median number of doses of oral psychotropic PRN medication administered by over 30%.Our balancing measures remained stable and we gained useful insights and development ideas from a staff survey.Further change ideas were planned for implementation over the months that followed, however, the impact of the COVID-19 pandemic meant that the project lost some momentum.Despite running into some difficulty over recent months, the team remain motivated to maintain and build upon our previous success.In the past few weeks, “Calm Cards”, , have been introduced.We hope that the outcomes of this intervention will be positive, both in terms of further reducing use of PRN medication and encouraging development of skills which can be utilised beyond the hospital environment.We also intend to share our learning with colleagues and explore the possibility of introducing the project to other wards within the hospital.


Although patient portals are widely used for health promotion, little is known about the use of palliative care and end-of-life (PCEOL) portal tools available for patients and caregivers.This study aims to identify and assess the user perspectives of PCEOL portal tools available to patients and caregivers described and evaluated in the literature.We performed a scoping review of the academic literature directed by the PRISMA  extension for Scoping Review and searched three databases. Sources were included if they reported the development or testing of a feature, resource, tool, or intervention; focused on at least one PCEOL domain defined by the National Coalition for Hospice and Palliative Care; targeted adults with serious illness or caregivers; and were offered via a patient portal tethered to an electronic medical record. We independently screened the titles and abstracts (n=796) for eligibility. Full-text  sources were reviewed. We abstracted descriptions of the portal tool name, content, targeted population, and reported user acceptability for each tool from included sources (n=19).In total, 19 articles describing 12 tools were included, addressing the following PCEOL domains: ethical or legal (n=5), physical (n=5), and psychological or psychiatric (n=2). No tools for bereavement or hospice care were identified. Studies have reported high acceptability of tools among users; however, few sources commented on usability among older adults.PCEOL patient portal tools are understudied. As medical care increasingly moves toward virtual platforms, future research should investigate the usability and acceptability of PCEOL patient portal resources and evaluate their impact on health outcomes. Patient portals are secure websites that provide access to personal health information and health care services that often include web-based tools for medical visits, health records, and medications . In addiPatients with chronic or serious illness may especially benefit from access to patient portals; in fact, having a chronic illness is a predictor of portal enrollment and use . Older aIn light of the potential benefits of digital health technologies for older adults and individuals with chronic or serious illness, interest is also growing in digital health interventions targeted toward palliative care and end-of-life (PCEOL) patients, including the use of patient portals . PalliatAs a first step in this line of research, we aim to conduct a scoping review of PCEOL patient portal research. The aims of this study are to (1) identify PCEOL patient portal tools available for patients and caregivers that are described and evaluated in the literature and (2) document patient and caregiver perspectives regarding these tools. By describing currently available tools and their respective user perspectives, this review intends to inform future research examining the association between tool use and PCEOL outcomes while guiding future PCEOL patient portal tool development.As little is known about the use of patient portals for the delivery of palliative and end-of-life care, a scoping review of the academic literature was conducted to identify sources describing currently available patient portal PCEOL tools. Our methods were aligned with methodological framework for scoping reviews by Arksey and O’Malley , which ipatient portal in combination with PCEOL terms such as palliative care, hospice, end-of-life, terminal illness, cancer, advance directives, symptom management, ACP, grief, and caregiver support. Our specific search strategy with exclusion terms is presented in To identify PCEOL patient portal tools described in the academic literature, we searched three databases  for peer-reviewed sources, including both qualitative and quantitative studies. The search terms included Sources were included in the scoping review if they (1) described a developed feature, resource, tool, or intervention that focused on at least one domain of PCEOL as defined by the National Coalition for Hospice and Palliative Care ; (2) tarPhysical aspects of care including pain and symptom management, as well as quality of life assessmentPsychological aspects of care, including care about anxiety, depression, stress, cognitive impairment, psychosocial support, coping skills, and support around patient or family grief and bereavementSocial aspects of care, including patient and family education and caregiver supportSpiritual and cultural aspects of care, including spiritual, existential, or religious support or attention to language, ritual, or dietary needsCare of the imminently dying, including end-of-life care and educationEthical and legal aspects of care, including ACP and documentation To determine whether sources met the inclusion criteria, 2 coders (MPI and CV) independently reviewed the academic sources by title and abstract.Full texts of sources included by title and abstract were downloaded and further assessed for eligibility by 2 coders (MPI and CV). We determined whether the portal tool was targeted toward patients, caregivers, or both by reviewing the full text of the included sources for explicit mention of the intended users of the portal tool. The full text was also reviewed for a thorough description of the tool’s purpose and available features, apps, and content. Sources that presented a protocol or planned portal feature, did not provide a detailed description of the resources or content , did not focus on a PCEOL domain, targeted a pediatric or non–seriously ill population, examined the user characteristics and perspectives of general portal use, were not published in English, described a tool that was not tethered to a patient portal , or for which the full text was not retrievable, were excluded from the review. Pediatric populations were excluded because of their different care needs, access to technology, and technological skills.The coders regularly reviewed the sources and discussed questions and possible disputes. Both coders agreed on the features included in the review, with a third reviewer (JDP) reconciling disputes. A summary of the search and screening processes is shown in The full text of each of the included sources was reviewed, and the elements and features of the described PCEOL patient portal tool were abstracted by 2 reviewers (MPI and CV) into a Microsoft Excel spreadsheet. To ensure consistency, the reviewers double-extracted five articles before dividing the remaining articles. The abstracted data included the following components: portal name, health system user, developer (research team or organization), target illness and/or palliative care element, intended audience , and a summary of the features. When described, patient and caregiver perspectives about the tool, including measurements and qualitative reports of acceptability or usability and user satisfaction, were abstracted.We used an iterative and mixed inductive-deductive approach to summarize the charted information and results from the included papers to address our aims . The chaThe initial database search identified 796 unique sources that we screened by title and abstract . At thisWe abstracted data from 19 sources published between the years 2013 and 2020 that describe 12 unique PCEOL patient portal tools . The ideAll five tools addressing ethical and legal aspects of care center on ACP, including ACP education and the opportunity to complete documentation of advance directive, or of an appointed MDPOA or health care proxy. The ACP tools in each of these patient portals provide education and a means to facilitate documentation of advance care preferences. We identified two tools (PatientSite and MyChart) that use a guided, patient-centered questionnaire to enable the completion of an advance directive ,33,34. AThe identified PCEOL patient portal tools addressing physical aspects of care focus on improving the quality of life of patients with serious illness. Four of the five tools identified allow users to complete patient-reported outcome assessments and other questionnaires electronically -46. The Similarly, MyAVL (MijnAVL), a patient portal for patients with breast and non–small cell lung cancer in the Netherlands, allows users to complete quality of life and physical activity questionnaires. Users are provided with tailored physical activity advice based on their responses. The commercially developed Carevive Care Planning System allows for the collection of patient-reported outcomes in patients with breast and gynecological cancers . TargeteOnly one of the tools addressing quality of life, the Patient-Centered Tool Kit (PCTK), was designed for use during acute hospitalization ,39. The Provider tool for patients with prostate cancer. This tool allows users to complete a questionnaire on distress levels and access treatment decision support resources [Two of the PCEOL patient portal tools addressed the psychological aspects of care, primarily providing resources for psychosocial support. The British Columbia Cancer Agency developed the esources . The tooesources . This toPortal use measures were described for nearly all (n=10) patient portal tools in at least one study. problem or condition was the least visited feature; however, this could be explained by the study’s in-patient setting where the patients and caregivers had more frequent access to providers [Overall, an overview of appointments and medical records were the most frequently accessed features for portals ,40,41,47roviders ,39.Studies for four of the portals specifically described methods of promoting portal use, either by reminders via electronic messages ,42,48 orUsability, acceptability, and user satisfaction were formally evaluated for nine of the identified PCEOL patient portal tools . Only twUsers viewed these tools as a means of improving communication. In particular, the integration of tools with electronic medical records was valued. In ACP tools, users appreciated that the advance directive forms were easily retrievable and viewable for their providers and family ,24,37. TPatient portals may offer accessible avenues for keeping individuals with serious illness and their caregivers connected to PCEOL resources. Although the use of patient portals among patients with serious life-limiting illness is of growing interest, previous studies suggest that patients with serious illness and their caregivers are potentially extensive users of patient portal tools that are commonly available, particularly to access care team information, review laboratory results, and obtain medical records ,51. InteThe results identified 12 unique patient portal resources supporting the following PCEOL domains: ethical or legal , physical , and psychological support (n=2). Most resources were targeted toward patient audiences (n=9), with the remainder being targeted toward caregivers, or both patients and caregivers. Only two resources focused specifically on the user characteristics of older adults ,49, whicEvidence demonstrates that patients living with serious illnesses and their family members and caregivers can benefit from palliative care . HoweverFurthermore, the role of digital health solutions, including patient portal resources, is more important than ever in the wake of the COVID-19 pandemic. Health systems across the United States, and the globe, are forced to incorporate digital care as part of their regular repertoire of available patient care as safety precautions and social distancing have become the norm ,56. IndiNearly all the portals included in this review focused on quality of life, symptom management, or ACP education or completion of advance directives. ACP may be better leveraged through patient portals than other resource types given its more limited and specific goal than some of the more complex and nuanced needs and components inherent in PCEOL care. Similarly, PROMIS tools and other electronic mechanisms for patient-reported outcomes are well suited for patient portals.Although the legal and physical aspects are essential pieces of quality and holistic PCEOL services, equally important are the psychological and spiritual aspects, as well as end-of-life education around hospice. Only two of the portals reviewed focused on the psychological needs of patients with serious illness ,49. NoneIt is important to emphasize the special consideration of older adult patients with serious illness and patient portal tool development, availability, and adoption among PCEOL populations. In total, 80% of older adults in the United States have one or more chronic conditions, many of which can lead to serious illness . As the To the best of our knowledge, this scoping review is the first to identify and describe patient portals that offer PCEOL features. However, this is not without limitations. First, the review only included results from academic literature and may be missing commercially available portals that were developed by industry without concurrent publication in the peer-reviewed literature. In addition, we did not evaluate the full text of articles in which we were unable to retrieve access, potentially overlooking additional PCEOL patient portal resources. Although some of the articles described the efficacy on clinically meaningful outcomes, we did not address this information as it was beyond the scope of our scoping view. Future research should focus on the efficacy of such tools. Finally, this review had a small sample size of articles (n=19) covering 12 different patient portals that are predominantly US based, potentially limiting the results to a Westernized PCEOL perspective.The scoping review highlighted an important gap in PCEOL resources offered via patient portals linked to electronic health records, as well as opportunities for leveraging patient portals as a means of offering education and support for patients with serious illness. The results suggest that there are ongoing efforts to offer various PCEOL supports through patient portals, particularly with a focus on ACP. However, our review resulted in a small number of portals that met our criteria, of which included only a few of the expansive elements of PCEOL care. Future research and development of patient portals would benefit from offering comprehensive PCEOL features to increase access and education for patients experiencing serious illness.


A substantial amount patients with cancer will develop bone metastases, with 70% of metastatic prostate and breast cancer patients harboring bone metastasis. Despite advancements in systemic therapies for advanced cancer, survival remains poor for those with bone metastases. The interaction between bone cells and the immune system contributes to a better understanding of the role that the immune system plays in the bone metastasis of cancer. The immune and bone systems share various molecules, including transcription factors, signaling molecules, and membrane receptors, which can stimulate the differentiation and activation of bone‐resorbing osteoclasts. The process of cancer metastasis to bone, which deregulates bone turnover and results in bone loss and skeletal-related events (SREs), is also controlled by primary cancer-related factors that modulate the intratumoral microenvironment as well as cellular immune process. The nuclear factor kappa B ligand (RANKL) and the receptor activator of nuclear factor kappa B (RANK) are key regulators of osteoclast development, bone metabolism, lymph node development, and T-cell/dendritic cell communication. RANKL is an osteoclastogenic cytokine that links the bone and the immune system. In this review, we highlight the role of RANKL and RANK in the immune microenvironment and bone metastases and review data on the role of the regulatory mechanism of immunity in bone metastases, which could be verified through clinical efficacy of RANKL inhibitors for cancer patients with bone metastases. With the discovery of the specific role of RANK signaling in osteoclastogenesis, the humanized monoclonal antibody against RANKL, such as denosumab, was available to prevent bone loss, SREs, and bone metastases, providing a unique opportunity to target RANKL/RANK as a future strategy to prevent bone metastases. Metastasis is a complicated, multistep process that is responsible for roughly 90% of cancer-related deaths . Bone meNormal bone development and maintenance are controlled by a balance in normal bone remodeling through bone-forming osteoblasts and bone-resorbing osteoclasts –10. In pOsteoclasts and osteoclast precursors express RANK, whereas RANKL is mainly produced by osteoblastic lineage cells , bone-maDating back to 1889, Stephen Paget postulated the idea of metastatic spread, that primary tumors induce changes to distant organs, due to the high degree of crosstalk between cancer cells and their microenvironment . The immBone metastases have traditionally included osteolytic and osteoblastic metastases classified according to the predominance of lysis or sclerosis in the bone. Growing evidence has suggested the fact that mixed-type bone metastases containing both osteolytic and osteoblastic change were also observed in most patients . Bone meOwing to the close connection between the immune system and the bone in many diseases, such as inflammatory diseases and postmenopausal osteoporosis, we focus on how the two systems affect each other. Evidence is accumulating to support many shared regulatory factors, including cytokines, receptors, signaling molecules, and transcription factors, between the immune and bone systems , 74 Fi. It has More findings provide evidence that the immune system performs a critical role in the progression and the treatment resistance of tumors. It is known that a complex interplay of immunity in primary tumors can have a profound impact on the development of tumors , 90. Hervia expression cloning techniques in the course of studying a novel tumor necrosis factor receptor (TNFR) superfamily member. RANKL, also known as TNFSF11 (tumor necrosis factor receptor super family 11), TRANCE (TNF-related activation-induced cytokine), OPGL (osteoprotegerin ligand), and ODF (osteoclast differentiation factor), was characterized as the cognate ligand of TNFR superfamily member  that are produced by bone marrow stromal cells and osteoblasts . RANKL oAlthough RANK expression was primarily found in osteoclasts and their progenitors, recent data also indicate that RANK is expressed on tumor cells, appearing to regulate metastases from establishing tumors . The rolvia PTHrP produced by breast cancer cells , vitamin D3, TNF-α, Wnt 5A, and Sclerostin (SOST), can promote osteoclastogenesis through inducing RANKL expression in osteoblasts, stromal cells, or T cells –163. TheMetalloprotease–disintegrin TNF-a convertase (TACE) , includivia regulating antitumor immune responses  targeting CTLA4, programmed cell death protein 1 (PD1), or programmed cell death 1 ligand 1 (PDL1) have not been prospectively unfolded in tumor bone metastasis patients. A few retrospective case series and case reports in bone metastatic melanoma have reported the synergism of ICI with denosumab. In case series including 10 patients with metastatic melanoma, the objective response rate was 60% and the disease control rate was 80% treated with denosumab and ICI (predominantly antiPD1 mAbs with or without antiCTLA4 mAbs), with the average treatment time of 9.8 months . FollowiAdvances in research about the early events of tumor seeding, dormancy, and local niche remodeling have provided new insights into the role of the bone microenvironment to facilitate the growth of metastatic tumors in the bone. The studies discussed above illustrate how the changes of the immune microenvironment contribute to bone metastases, providing a highly modular platform potentially applicable to a broad range of cancer bone metastases. New molecules targeting bone metastases should combine with immune modulation in order to achieve effective eradication of metastatic lesions with minimal side effects.In the past decades, many studies have uncovered the biological network associated with RANKL–RANK in immune systems, the development of lymphoid organs, bone metastasis regulation, and the initiation and progression of sex hormone-driven mammary cancer. Now that RANKL inhibitors to block osteoclast differentiation could be effectively used in treatments of osteoporosis, bone loss, and bone metastasis, the knowledge about how to modulate RANK signaling is needed to be further excavated and long-term clinical studies are needed to estimate whether the combined use of inhibitors with conventional therapies or ICIs leads to optimal treatment for bone metastasis patients.BL and PW provided direction and guidance throughout the preparation of this manuscript. JJ and HW conducted the literature review and drafted the manuscript. PZ and WX reviewed the manuscript and made significant revisions on the drafts. All authors read and approved the final manuscript.This work was supported by grants from the National Natural Science Foundation of China (81902733), the Shanghai Science and Technology Committee , and the Sailing Plan Project of Shanghai Municipal Commission of Science and Technology (19YF1448100).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In a recent meta-analysis of interventions for dementia caregivers, psychoeducational interventions were found to be effective only if they required caregivers to apply knowledge and skills through active engagement. This emphasizes the importance of understanding which intervention components enhance application in order to improve caregiving interventions and the mechanisms by which they work. The purpose of this presentation is to identify and assess elements of active engagement within dementia caregiving interventions. Articles included in this review were published between 2009 and 2018 and identified as psychoeducational dementia caregiving interventions. Each intervention was assessed to describe: 1) how active engagement was defined, 2) the logistics for implementing the active engagement techniques, 3) and the process for evaluating active engagement components. Of 36 articles meeting inclusion criteria, 25 mentioned active engagement components of the intervention. Active components included discussion, problem-solving, practice, role-play, action plans, and homework. Only five articles provided partial descriptions of the active components, five mentioned assessing active engagement, and only one study examined the efficacy of an engagement technique. This demonstrates a significant gap in our understanding of interventions for dementia caregivers. Active engagement enhances outcomes, yet to our knowledge, the specific steps taken to engage caregivers actively and the mechanisms by which these work are unclear. This is a barrier to optimizing active engagement within intervention delivery. Clarifying processes and methods for testing mechanisms of action can further enhance caregiver engagement with interventions.


Older adults in rural areas are at unique risk for poor outcomes due to social isolation and limited access to resources. The Mississippi High Obesity Program (HOP) aims to enhance access to social connections and resources like community gardens, food pantries, and physical activity as part of its broader objective to prevent and reduce obesity. Through policy, systems and environment strategies, development of Memoranda of Understanding (MOUs) between aforementioned entities, and community based participatory research approaches, Mississippi HOP efforts enhance food systems improvement efforts; grow multi-sectoral collaboration; and evaluate the effectiveness of new policies, and specifically MOUs, in reaching these goals. Older adults represent more than 40% (n=27) of all coalition members and stakeholder leaders (n=61); they are essential for the success of these initiatives. This presentation will highlight the work done during the COVID-19 pandemic and the role of and benefits to older adults, especially ones in rural communities.


To determine the effects of a tailored quality improvement programme for effective medication management including a reduction in prescription and administration errors in oral and depot psychotropic medication, patient education on medication and implementation of policies and guidelines.Medication errors are common in hospital admissions and pose a threat to patient safety . Medication errors may occur in different stages of the patient treatment process such as during prescribing, transcribing, preparing, dispensing, administration, and monitoring . In addition to these, for the detained mental health patients, the Mental Health Act 1983 legislation requires up-to-date treatment certificate compliance . A Quality Improvement programme to improve safe medication prescription and administration was designed for the patients admitted in Delfryn House, a mental health high dependency rehabilitation unit.Using Plan-Do-Study-Act (PDSA) quality improvement methodology, a medication management committee was created under the leadership of Specialty doctor and Head of Care (HOC), and comprising of the consultant psychiatrists, specialty doctor, heads of care (ward managers), senior nurses, pharmacists, hospital manager and hospital director. The committee reviewed the medication errors reported in the last year and planned the Pre-Intervention Phase 1 and Post Intervention Phase II Audits.The Intervention project was broadly divided into two domains---Doctors’ Prescription led by the Specialty doctor and the Nurses’ Medication Administration, led by the Head of care. Using the QI “theory of change” model, three primary drivers of “Safe Prescription and Administration”, “Patient Education” and “Policies and Guidelines Implementation” were established. The poster will have a demonstration of the complete drivers’ diagram.Secondary drivers for “Safe prescription and administration” required inputs from doctors, nurses and pharmacists; Change ideas (Interventions) of introducing In-patient depot clinics, Daily 10-Points self-audit by clinic nurse, twice daily information about patients’ medication compliance in morning and evening electronic handovers, PDSAs with monthly audits of prescription and administration errors, monthly pharmacists’ audits for drug interactions and monitoring of adverse effects and rapid tranquilisations were implemented.Secondary drivers and change ideas for “Patient Education” included discussions with Multidisciplinary teams, medication information leaflets being available to patients, discussion slots with pharmacists, self-administration of medication, and alternate self-management strategies instead of PRN medications.Secondary drivers and change ideas for the “Policies and Guidelines Implementation” included steps to ensure all staff were aware of the policies for safe drug administration, rapid tranquilization and PRN utilisation, medication meetings minutes being circulated to all staff, and monthly audits for MHA1983 Section 57 treatment certificates for detained patients.The medication Management Committee continued to meet on monthly basis to review the interventions, implementation of new strategies, and new recommendations on the basis of monthly mini-audits. A patient satisfaction survey on their knowledge about prescribed psychotropic medication was also conducted pre and post-intervention.Results of Phase I and Phase 11 were compared. There was a significant reduction in prescription errors by doctors (19% to 3%) and medication administration (34% to 11%). Mental health documentation compliance improved from 77% to 98%. Patient satisfaction survey also demonstrated more knowledge about their prescribed psychotropic medication (15% to 32%). Two areas however did not show satisfactory improvements; There was not a significant improvement in acknowledgment or documentation of potential drug interactions or adverse events raised by pharmacists. Errors related to depot medication administration reduced in the initial two months, but increased again. The introduction of the Weekly Depot Clinic was not found successful by the administering nursing staff, and it was moved back to daily administrations.The formation of the medication management committee and the quality improvement programme showed significant improvement in most areas of effective medication management. The primary and secondary drivers with the change ideas gave structure to the intervention programme. The mini-audits using PDSA methodology helped to test different interventional strategies and to assess their impact and building upon the learning from previous results. This shows that for sustained effective medication management, this should not be a one-off exercise, and we need to continue learning and implementing newer strategies for continued effective medication, taking on-board the advice from MDT, nursing, patients, and carers.