Cytomegalovirus (CMV) infection during pregnancy may result in long-term health problems for children with congenital CMV (cCMV). Currently, no prevention or treatment interventions are approved by the Food and Drug Administration for a cCMV indication. Healthcare provider and public awareness is low, and formal clinical practice guidelines and local practice patterns vary. A pilot study of eight cCMV experts was performed using qualitative semi-structured interviews to better understand clinical practice guidelines and patterns in the United States. Results from participant interviews highlighted the need for better prenatal diagnostic techniques, broader neonatal screening opportunities, and more robust evidence supporting intervention strategies. Healthcare provider and public partnerships are essential for advancing cCMV guidelines and improving care delivery. Our results provide a preliminary knowledge base and framework for developing a consensus cCMV research agenda to address evidence gaps that limit the revision of clinical practice guidelines. The changes in clinical practice patterns that may arise as a result of further research have the potential to reduce risk during pregnancy and improve care for children with cCMV infection. Human cytomegalovirus (CMV) is a ubiquitous β-herpesvirus that infects most people worldwide at some point during their lives and establishes lifelong infection ,2,3. CMVApproximately 10% to 15% of neonates with cCMV infection are born with clinically apparent effects such as jaundice, microcephaly, low birth weight, or sensorineural hearing loss, while the remainder of neonates with CMV infection appear clinically normal at birth ,12,13. FAlthough many evidence-based clinical practice guidelines (CPGs) for pre-pregnancy and maternal care have been published, few provide comprehensive recommendations for cCMV prevention. The US-based American College of Obstetricians and Gynecologists (ACOG) and Society for Maternal-Fetal Medicine (SMFM) each have issued CPGs related to cCMV; however, these CPGs were last updated in 2015 and 2016, respectively, and thus do not reflect more recent studies ,25. WhilVariability among the cCMV CPGs presents an opportunity to update and align them, particularly for prenatal risk reduction. Moreover, understanding CPG utilization cannot only support progress in clinical practice but also guide public health policy. We therefore conducted a qualitative assessment of cCMV CPGs and practice patterns from the perspectives of healthcare professionals and research leaders with expertise in the field of cCMV infection and disease.A qualitative semi-structured interview study was performed to establish an understanding of cCMV CPGs and practice patterns in the United States, including primary and non-primary CMV infection in pregnant people, from the perspective of cCMV research and clinical experts. Interviews were conducted by video conference between 17 March and 17 June 2022.With the aim of recruiting 8–10 professional society members with experience developing CMV CPGs, 15 professionals with expertise in a cCMV-related healthcare field were identified. A letter of invitation was sent via email to recruit the identified professionals between 1 March and 8 May 2022; the experts who indicated their willingness to participate in the study via a response email were contacted by the study researcher to arrange an interview. The participants represented a balanced mix of professional society membership, with ACOG, SMFM, and the American Academy of Pediatrics (AAP) represented most. Seven participants were from diverse geographic regions of the United States, and one was from Canada.Interviews were conducted by one of the authors (SK), along with at least one other non-participant attendee. Interviews lasted approximately 1 h and were concluded when data saturation was achieved. Information regarding participant training, clinical specialty, research activities, and participation in CPG development was collected.A semi-structured interview schedule consisting of 9 open-ended questions was used to obtain information from experts, and related prompts were used to guide the direction of interviews. “Clinical practice guideline(s)” referred to formal recommendations published by a professional society or group of experts. “Clinical practice patterns” or “standards of care” referred to patient management approaches that can vary by provider or region and may not follow CPGs exactly. Interview questions included but were not limited to: (a) Which clinical practice guidelines and organizations inform cCMV care? (b) What would drive change in clinical practice guidelines and patterns for CMV screening and prevention for pregnant people? (c) What is needed to drive changes in guidelines and approaches to care for patients with cCMV infection? The interviewer was permitted to follow and encourage elaboration on comments spontaneously introduced by participants to allow more detailed information from different fields of healthcare.Interview responses were analyzed with NVivo , a qualitative analysis software package that facilitates the identification and classification of themes in interviews . Themes n = 7) and Canada (n = 1). The US participants were from the Midwest, South, West Coast, East Coast, and Mid-Atlantic regions, providing balanced geographic and demographic representation. The following professional societies were represented by participants: AAP, SMFM, and ACOG. The Canadian participant was a member of SOGC and was included to provide a comparison with US guidelines. Seven of the participants were practicing physicians with expertise relevant to cCMV infection, and one was an epidemiologist with extensive research experience in CMV. Half of the participants (n = 4) specialized in pediatrics, and the other half specialized in obstetrics and gynecology  and maternal-fetal medicine .A total of 15 experts were contacted, and eight agreed to be interviewed. The interviews were conducted with eight healthcare professionals and research leaders in the United States (n = 7), and the guidelines from AAP and SOGC were each referenced by half of the participants , most frequently the SOGC (n = 5), France (n = 3), Australia (n = 2), New Zealand (n = 1), and Italy (n = 1), regarding their distinct screening guidelines and research priorities.Differences in CPGs and practice patterns between the United States and other countries were discussed by the majority of participants  to offer children with infection. Three participants  expressed the perception that universal newborn cCMV screening is not cost-effective , which hn = 3) and lower prioritization of cCMV compared with other concerns during pregnancy (n = 2). Neither of the two OB/GYNs interviewed indicated that cCMV was irrelevant or a lower priority in their own clinical practices compared with other perinatal diseases and concerns  noted that more robust diagnostic approaches that produce fewer false positives would encourage providers and likely professional organizations to make CMV screening standard practice for pregnant people and neonates. Consistent with this perspective, routine CMV screening in pregnancy is not recommended in CPGs from US societies or international consensus groups, which also cite a lack of appropriate screening methods or interventions [n = 7) indicated that evidence demonstrating an effective intervention to reduce fetal CMV transmission would be the major factor prompting changes in both CPGs and clinical practice patterns  indicated that raising both public and professional awareness is particularly crucial for advancing cCMV care in pregnancy and childhood. The Centers for Disease Control and Prevention (CDC), ACOG, and the American Board of Obstetrics and Gynecology (ABOG) were identified as influential bodies that could improve awareness and thus serve as vital stakeholder partners  that patient advocacy groups are essential partners in the effort to advance cCMV care. Experts highlighted that professional societies have begun to include patient voice in their decision-making process and that patients are generally accepting of preventative measures and willing to implement them to reduce risk  have mandated targeted cCMV screening based on failed hearing evaluation, and one state (Minnesota) has legislated universal newborn screening for cCMV ,32. AlthAlthough the study size was small and participant responses were intrinsically anecdotal, the semi-structured nature of interviews focused their attention on areas that were highly relevant to patient care. Furthermore, the study group had broad representation across the United States, including the Midwest, South, West Coast, East Coast, and Mid-Atlantic regions, as well as in Canada. In addition to the small study group and nature of the data, a limitation of this study was inherent bias from the generally similar perspectives among participants towards current CPGs. Furthermore, the inclusion of participants who were largely affiliated with academic hospitals may limit generalization of their views to other practice settings. The results from this pilot study call for the exploration of opinions among healthcare providers from other specialties serving various populations and across global regions. We consider the next steps to be identifying knowledge gaps through a systematic review of the published literature and building a consensus around a core cCMV research agenda based on key gaps. This process should be transparent and inclusive, with the involvement of healthcare providers, researchers, industry representatives, affected patients and families, and other cCMV stakeholders. Targeting a broad audience, including funders, this effort should aim to clarify and prioritize the many important areas of cCMV clinical practice and research, ranging from awareness and public health to screening, antiviral therapies, and vaccine development. All stakeholders affected and/or motivated by cCMV share this common vision in which optimal care will ensure that children are born without a potentially devastating CMV congenital infection.


The advancements and abundance of mobile phones and portable health devices have created an opportunity to use mobile health  for population health systems. There is increasing evidence for the feasibility and acceptance of mHealth with Indigenous populations. Providing a synthesis of qualitative findings of mHealth with Indigenous populations will gain insights into the strengths and challenges to mHealth use in Indigenous populations.This review aimed to identify and synthesize qualitative data pertaining to the experiences and perceptions of mHealth from the perspectives of end users (patients and service providers) living in the colonial settler democracies of Canada, Australia, New Zealand, the United States, the Pacific Islands, and the Sápmi region of northern Europe.In May 2021, systematic searches of peer-reviewed, scientific papers were conducted across the 5 databases of PubMed, CINAHL, Embase, PsycINFO, and Web of Science. Qualitative or mixed method studies were included where a mHealth intervention was the primary focus for responding to health challenges with Indigenous populations. Two authors independently screened papers for eligibility and assessed the risk of bias using a modified version of the Critical Appraisal Skills Programme. A meta-aggregative approach was used to analyze the findings of included studies.Seventeen papers met the eligibility criteria, 8 studies with patients, 7 studies with service providers, and 2 studies that included both patients and service providers. Studies were conducted in Australia (n=10), Canada (n=2), New Zealand (n=2), Papua New Guinea (n=1), the United States (n=1), and Samoa (n=1). Our interpretation of these qualitative findings shows commonalities between Indigenous patients’ and service providers’ perceptions of mHealth. We summarize our findings in six themes: (1) mHealth literacy, (2) mHealth as a facilitator for connection and support, (3) mHealth content needed to be culturally relevant, (4) mHealth security and confidentiality, (5) mHealth supporting rather than replacing service providers, and (6) workplace and organizational capacity.This research suggests that mHealth can meet the needs of both patients and service providers when the mHealth intervention is culturally relevant, accounts for digital and health literacy, incorporates interactive components, is supported by workplaces, fits into health provider workflows, and meets security and confidentiality standards. Future mHealth research with Indigenous populations should partner with key representatives  in the mHealth design appropriate to the purpose, people, setting, and delivery. The technological advancements and abundance of mobile phones and portable health devices have created a plethora of mobile health  tools. mHealth is defined as “the use of mobile devices—such as mobile phones, patient monitoring devices, personal digital assistants and wireless devices—for medical and public health practice” . These iSystematic reviews globally have suggested mHealth is a broadly feasible and effective resource for a range of health conditions including; behavior change ,3, noncoThere is a growing number of qualitative studies exploring the views and perceptions of mHealth from 2005 onward, resulting in a number of qualitative systematic reviews -21. FindProviding a synthesis of qualitative findings of mHealth with Indigenous populations will gain insights to the strengths and challenges to mHealth use in Indigenous populations. This review aimed to identify and synthesize qualitative data pertaining to the experiences and perceptions of mHealth with Indigenous populations and the service providers that work with Indigenous populations.A systematic search was conducted of peer-reviewed literature for this qualitative synthesis. A protocol of this qualitative synthesis was registered with the International Prospective Register of Systematic Reviews . We extracted qualitative data pertaining to the experiences and perceptions of both patients (Indigenous peoples) and service providers  who work with Indigenous peoples from Canada, Australia, New Zealand, the United States, the Pacific Islands, and the Sápmi region of northern Europe. We define Indigenous Peoples as “distinct social and cultural groups that share collective ancestral ties to the lands and natural resources where they live, occupy or from which they have been displaced” [A comprehensive list of search terms and strings were developed with the assistance of a librarian with expertise in systematic reviews. Systematic searches of peer-reviewed, scientific papers in English were conducted across 5 databases in May 2021: PubMed, CINAHL, Embase, PsycINFO, and Web of Science. Qualitative or mixed method studies were included where a mHealth intervention was the primary focus for responding to health challenges with Indigenous populations. As such, experimental and quasi-experimental studies were considered, as long as they met the following inclusion criteria:Participants: Indigenous people of all ages from Canada, Australia, New Zealand, United States, the Pacific Islands, the Sápmi region of northern Europe; OR are service providers (either Indigenous or non-Indigenous) who work with Indigenous persons from Canada, Australia, New Zealand, the United States, the Pacific Islands, the Sápmi region of northern Europe; OR where participants are multicultural, outcomes for Indigenous persons are reported specifically.Interventions: primary focus was a mHealth intervention delivered using a wireless device . The mHealth intervention aims to address a health challenge .Outcomes: studies reported on one or more outcomes including user; experiences, perceptions, barriers, and enablers via qualitative research methods .A sample of the search strings using text words and subject heading keywords for PubMed can be found in Initial database searches and duplicate removal were conducted by 1 author (AG). Screening, review, and extraction were assisted by the web-based systematic review program Covidence  . Two autSubsequently, 2 authors (AG and SL) screened the full-text papers independently and then discussed for comparison. Any differing views were resolved through discussion. Manual searches of reference lists were conducted on full-text papers included in the review. A final list of full-text papers and their citations which met inclusion criteria were downloaded and saved using Covidence software.The quality of the included studies was appraised using a modified version of the Critical Appraisal Skills Programme (CASP) qualitative checklist . An addiThe data included in the analysis were all text included in the “Results” or “Findings” sections of the papers (excluding purely quantitative findings) and was extracted from the papers into NVivo 12 Plus software  . CharactA meta-aggregative approach was used to analyze the findings . This anFrom database searches, 2608 unique papers were identified; 2 additional papers were located by manual searches. In total, 2610 titles and abstracts were reviewed against the inclusion criteria, of which 2548 were excluded, leaving 62 papers for full-text review. Following the full-text review, 45 papers were excluded, leaving 17 studies included in this qualitative systematic review .All 17 studies included in this review were published between 2013 and 2021. Eight were studies specifically with Indigenous patients -38. SeveCharacteristics of the 17 studies are shown in Our interpretation of these qualitative findings shows commonalities between Indigenous patients and service providers perceptions of mHealth. We have collectively termed both as “end users” hereafter unless explicitly stated otherwise. Common themes across end users were: the importance of mHealth or digital literacy, mHealth as a facilitator for connection and support, mHealth content that needed to be culturally relevant, and data security and confidentiality. Two themes emerged that were unique to service providers including the importance of mHealth supporting rather than replacing service providers, and the role of workplace champions and organizational capacity for influencing uptake and sustainability of mHealth.Access to the required hardware (mobile or smartphones and touch screen tablets) and systems (network coverage and IT) was identified as an important influence to mHealth uptake by end users. Service providers noted barriers to accessing the mHealth hardware and systems with reasons including regionality and workplace restrictions ,43,45,46Low levels of IT literacy pose a challenge to electronic mental health adoption. Unfamiliarity with different ways of using technologies impedes the utilization of the approach by both service providers and community members. Poor IT literacy within communities was attributed to limited access to technology…Limited confidence in using new technology such as mHealth initiatives was identified as a barrier to uptake for service providers ,44. The Age and generational implications were raised as influential factors to the uptake of mHealth. Whether implicitly or explicitly, end users perceived mHealth would be more applicable and accepted by younger people -42,44,46Most older interviewees did not appear to have any major issues with knowledge on how to access phone features.End users noted the importance of mHealth resources easy to understand and use for a confident user experience. A notable motivation for service providers to use a mHealth resource was for it to be uncomplicated and easy to use ,40,45-47Participants spoke of parents being technologically savvy, and parents referred to accessing apps, YouTube clips, social media and the internet from their mobile phones for infant feeding information and support prior to the Growing healthy program.The incorporation of visual and audio capabilities was suggested by end users to create a better understanding of the content ,44,46-48Several interviewees described how the iECG device provided unique opportunities to engage patients in education around AF and their heart, and to empower patients to find out more about their heart health.End users found mHealth an appropriate resource to facilitate engagement, connection, and support within health care systems. Indigenous patients appreciate that mHealth facilitated connection to support people, along with health care providers -38,47,48mHealth was found to provide a sense of reassurance and encouragement across a range of health journeys for Indigenous patients, including perinatal health ,47, patiParticipants valued sharing advice and experience-based information with their families, partners or wider virtual communities, such as Facebook groups.Service providers found that mHealth encouraged trust with patients while creating a collaborative environment with other health staff -42,46. mCommunication across services working with the same client may help to ensure nonoverlapping of interventions and resources.Australian service providers noted mHealth broke down the barriers of patient engagement, “equalising the power imbalance often present in their relationships with clients” . ServiceSome may have felt known in a small community or simply hesitant to engage a service because they felt uncomfortable. The app allowed them a choice in health care that was previously unavailable.End users stated the importance of mHealth including culturally relevant imagery and language to enable engagement, trust, and relatable connection. The inclusion of culturally relevant language and imagery was important for Indigenous patients to encourage engagement and build trust in mHealth content ,38,46,47…participants were keen to engage with apps that included Māori language, tikanga and knowledge.Culturally relevant graphics, voices, animation, and optional short video clips may assist in engagement with the content, improve understanding, and overcome literacy issues.Recommended features of a technology resource included a look and feel that was user-friendly, aesthetically pleasing , easy to read, quick to navigate, and interactive .Yet, the acknowledgment of diversity in cultural relevance was an important implication noted by Indigenous patients ,33,38, nFindings suggest mHealth can assist in developing cultural competence through gaining a better understanding of cultural diversity, histories, and traditional languages. In Australia, Indigenous patients advised the importance of including cultural determinants such as colonization, intergenerational trauma, and identity within mHealth content ,38. In ARespondents expressed gratitude that the app now exists as an important tool for use in training and orienting new hires to Nunavut’s cultural and language context.Security and privacy consistently emerged with Indigenous patients across several studies with differing views and implications ,35,37,46…emphasised privacy concerns whereby they encountered personalised advertising on Google and Facebook that was based on previous searches done on the device.Importantly, mHealth offered the opportunity of anonymous support for patients wishing not to engage with health services face to face ,34,37. AThe ability to interact with the app privately, without anyone else needing to be present, meant that youth who may have been reluctant or afraid to speak to family members or health care professionals in a face-to-face setting could still access support.Service providers stated the importance of mHealth needing to support established workloads and practices rather than being an onerous addition to established workloads. Service providers raised uncertainties about the sustainability of mHealth, and how their roles and responsibilities may change with the implementation of mHealth -42,44,46Gatekeepers less experienced in suicide prevention may find a resource more useful than more experienced or confident gatekeepers.Service providers saw the benefit of mHealth as an educational tool to develop skills and knowledge. Service providers in Australia liked the health promotion opportunity a smartphone-enabled electrocardiogram (ECG) provided . ServiceSome staff also spoke of how using the device for screening led them to want to learn more about AF and cardiovascular disease themselves in their professional role.Workplace leadership, capacity, and strategic direction emerged as influencing factors to the uptake and sustainability of mHealth for service providers working with Indigenous populations ,42,44,45Workplaces that have leaders and champions to drive and support mHealth were a central factor in enabling mHealth uptake. The presence of enthusiastic managers and eager IT champions had a positive effect on the workforce’s interest in mHealth resources with service providers in Australia ,44. WorkHaving leaders within the organization showing interest and providing direct support was perceived to facilitate uptake. It created incentives and provided opportunities for service providers to reflect and evaluate the utility of the electronic mental health approach.Workplace staff capacity and retention contributed to the opportunities service providers had to commit to mHealth implementation ,42,44. H…in many services, demanding workloads left the workers with little or no opportunity to incorporate new skills into their existing work practices…A workplace culture that supports and drives the use of health innovations was shown to positively impact service providers’ perception of mHealth. The absence of health innovation priorities in workplace strategies caused a sense of ambivalence and ineptness toward the need for mHealth among service providers ,42,44. WUptake of electronic mental health approaches was dependent upon the perceived fit of the innovation to the organization’s priorities.This review found that both Indigenous patients and service providers are enthusiastic about the role that mHealth can play in health service delivery.Common themes across end users were: importance of mHealth or digital literacy, mHealth as a facilitator for connection and support, mHealth content needed to be culturally relevant, and data security and confidentiality are a priority. Two themes emerged that were unique to service providers: the importance of mHealth supporting rather than replacing service providers and the role of workplace champions and organizational capacity for influencing the uptake and sustainability of mHealth.In this review, most included studies stated the importance of relevant cultural imagery and language, which enabled greater comprehension of mHealth messaging and increased engagement by end users ,44,46,47Similar to the included study findings of this review, mHealth has been shown to enable patients to engage with their health care providers more effectively as well as connect with peers on similar health care journeys . MoreoveOur findings showed the importance of workplaces and their leadership in influencing the uptake of mHealth ,42,44,45Other systematic reviews conducted to understand the influencing factors to mHealth uptake show a strong correlation with the findings presented here. Namely, the principal influencers for adoption are the mHealth design, personal perceptions of mHealth, and the workplace environment ,21,52, wEarly engagement with the Indigenous community within eHealth research and implementation has shown to offer the greatest opportunity for acceptability, and local advocacy ,23,53,54In conclusion, there has been considerable growth in qualitative research exploring contextual factors in relation to mHealth uptake in non-Indigenous populations, yet less so for Indigenous populations. To our knowledge, this is the first review of qualitative studies that provides an understanding of the influential factors for both patients and service providers for Indigenous populations in relation to mHealth.Having 2 reviewers from diverse cultural backgrounds and gender orientations independently screening improved the quality of this meta-synthesis. The authors are a multidisciplinary team with a breadth of expertise in this review focus . Using a meta-aggregative approach to analyze the findings ensured cultural learnings identified by researchers’ conducting the original studies were not lost by the reviewers. The quality appraisal tool used a modified version of the CASP qualitative checklist, with the additional question locating the researchers’ cultural or theoretical standpoint, improving the cultural rigor of this critical appraisal tool. Most studies were of medium to high quality, and the quality appraisal tool can be found in Our review has some limitations; first, the searches were restricted to peer-reviewed literature published in 5 databases . Second, publication bias may have occurred due to the subjective quantifying of studies reporting on one or more outcomes via qualitative research methods. Finally, the results of this study are based on the meta-synthesis of qualitative data, which is inherently subjective. There are studies included from all countries (except the Sápmi region), but there are still only a few studies in each country, and so more work is needed. Papers need to report not only on patients’ perspective but other end users to gain a full understanding of the perceptions of mHealth in supporting health care with Indigenous populations.This review used meta-aggregation to summarize the findings of 17 qualitative studies on the experiences and perceptions of mHealth with Indigenous populations and the service providers that work with Indigenous populations. mHealth end users are enthusiastic about the role that mHealth can play in Indigenous health service delivery. There is a need for mHealth design to center end users within a co-designed approach with Indigenous people. There is recent work driving this agenda in an Australian context . Allowin


For patients with complex health and social needs, care coordination is crucial for improving their access to care, clinical outcomes, care experiences, and controlling their healthcare costs. However, evidence is inconsistent regarding the core elements of care coordination interventions, and lack of standardized processes for assessing patients’ needs has made it challenging for providers to optimize care coordination based on patient needs and preferences. Further, ensuring providers have reliable and timely means of communicating about care plans, patients’ full spectrum of needs, and transitions in care is important for overcoming potential care fragmentation. In the Veterans Health Administration (VA), several initiatives are underway to implement care coordination processes and services. In this paper, we describe our study underway in the VA aimed at building evidence for designing and implementing care coordination practices that enhance care integration and improve health and care outcomes for Veterans with complex care needs.In a prospective observational multiple methods study, for Aim 1 we will use existing data to identify Veterans with complex care needs who have and have not received care coordination services. We will examine the relationship between receipt of care coordination services and their health outcomes. In Aim 2, we will adapt the Patient Perceptions of Integrated Veteran Care questionnaire to survey a sample of Veterans about their experiences regarding coordination, integration, and the extent to which their care needs are being met. For Aim 3, we will interview providers and care teams about their perceptions of the innovation attributes of current care coordination needs assessment tools and processes, including their improvement over other approaches (relative advantage), fit with current practices (compatibility and innovation fit), complexity, and ability to visualize how the steps proceed to impact the right care at the right time (observability). The provider interviews will inform design and deployment of a widescale provider survey.Taken together, our study will inform development of an enhanced care coordination intervention that seeks to improve care and outcomes for Veterans with complex care needs. Care coordination has proved essential for improving patients’ access to care , clinicaIn the VA, evidence on care coordination for patients with complex care needs has been emerging. In the randomized trial of enhanced care coordination services within the VA Patient Aligned Care Team (PACT) Intensive Management (PIM) model, adding care coordination to the established and effective PACT model , 22, 23 Despite this evidence, the extent to which care coordination is contributing to outcomes is not fully known. The core elements of care coordination interventions for patients with complex care needs have varied considerably across interventions and patient populations , 21. CorThe purpose of this paper is to outline what is known and not known about care coordination and its impacts among Veterans and describe our study underway in the VA. We will conduct a prospective observational multiple methods study, which will gauge the relationship between care coordination services and subsequent healthcare use among Veterans with complex care needs, assess Veterans’ perspectives about care integration in the services they have received, and assess provider perspectives about care integration and care coordination tools and processes. We describe potential limitations to our approach as well. Finally, we highlight how our approach may inform evidence building in other health care systems that can inform implementation of an improved care model that incorporates these perspectives, addresses the identified needs, and focuses care on aspects with the greatest potential to improve patient outcomes.In our prior work, we explored the linkages between care coordination theory and practice and considered three specific domains—the context surrounding an intervention, an intervention’s locus , and elements of its design (mechanisms and types of interventions) , 30. The1.At a population level (and without intervention), what are the predicted outcomes for each risk group as determined by a care coordination needs assessment?2.What is the effect of a needs-assessment-determined risk group intervention (assigned care coordination resources) on outcomes .3.In the absence of intervention, what proportion of patients are categorized into the “wrong” risk group (and proceed to have outcomes at different levels/rates than expected)?4.What are the associations between care coordination services and receiving the “right care at the right time”?5.Even with care coordination services, what proportion of patients do not receive any care or optimal care ?6.What is the effect of “right care at the right time” on outcomes ?To operationalize and develop measures to examine how care coordination relates to patient outcomes, we viewed care coordination as a process with a sequence of steps and corresponding outcomes to be measured. With this guiding logic model, our multiple methods study comprises three aims intended to begin to address some but not all aspects. For Aim 1, we will focus on components 1–3 in the logic model by characterizing and comparing relationships between care coordination processes and health and care outcomes among Veterans with complex care needs. We will begin to address components 4 and 5 by surveying Veterans about their experiences regarding care coordination and the extent to which their needs were met (Aim 2). We will also interview and survey providers (Aim 3) about their perceptions of the attributes of care coordination needs assessment tools and processes that VA sites have implemented, including their improvement over other approaches (relative advantage), their fit with current practices at their facility (compatibility and innovation-fit), complexity, and the ability to visualize how the steps proceed to impact the right care at the right time (observability). To assess the extent to which the right care happens at the right time to potentially impact outcomes (component 6), we will combine information gathered from all three aims to describe scenarios that will exemplify the need for a future implementation study. Taken together our approach will inform development of enhanced care coordination processes with a goal of improving care integration and patient outcomes.As a multiple methods study, we have three aims, each with specific research questions, hypotheses, and design aspects. The design and methods for each study aim follows.In aim 1 our main research question is “Are care coordination services associated with differences in health and care outcomes, including emergency department (ED) and urgent care (UC) visits, hospitalizations, mortality, and healthcare costs among Veterans with complex care needs?” We hypothesize that receipt of care coordination services will be associated with fewer ED/UC visits and hospitalizations, decreased mortality, and reduced or neutral healthcare costs among these Veterans.We will characterize and compare the relationship between receiving care coordination services and subsequent healthcare use, health outcomes, and healthcare costs among Veterans with high-risk, high-need conditions in a prospective observational study. With existing data available in the VA's electronic health record (EHR), VA-purchased care claims, and Medicare claims data from the Centers for Medicare and Medicaid Services (CMS), we will identify and match Veterans with complex care needs with and without care coordination services based on clinical and sociodemographic characteristics and prior healthcare use. The VA lacks a standardized care needs assessment in the EHR, but does include a means to identify Veterans with high risk for hospitalization and/or mortality, based on annual care assessment of needs (CAN) scores . Those wTo assess use of care coordination services, we will rely on information from VA documentation of service provision. In the VA, care coordination is documented as specific services in accordance with CMS coding and using Current Procedural Terminology (CPT) codes, including transitional care management, chronic care management and complex chronic care management codes and with detailed requirements for who can deliver the services and how frequently the services can be reported for CMS. VA care is not subject to the CMS frequency limits, however. In planning this study, we queried the VA Corporate data warehouse to gauge the volume of care coordination workload in selected sites involved in the early phase of VA Care Coordination and Integrated Case Management initiative, which aimed to begin standardizing care coordination assessment across VA facilities . For AimPilot data from FY2018 from one VA medical center were used to estimate a conservative sample size of cases (those with a needs assessment documented) and comparator patients for at least 12 VA facilities. We estimated a minimum of 88 cases per site; and with a 1:1 match for comparators, a total sample of 2,112. With growth in the number of VA sites expanding and documenting care coordination services, we expect this number to be easily surpassed during the observation period (FY2019–FY2021).In the pilot data, there were an average of 15.5 ED visits per year in Veterans with care coordination. We estimated Veterans without care coordination to have 26 ED visits per year. With an estimated sample size and ED visit rates, we calculated 98.6% power to detect a difference of 20% in ED visits per year between those with and without care coordination, which is in line with prior studies .We will estimate annual total healthcare costs (VA and Medicare), including outpatient, ED, UC, inpatient, and medication costs. For power calculation estimates, we rely on Yoon et al. , which iWe considered experience of Wang et al. , and ourt-tests or Chi-squared tests. We will test the hypothesis that outcomes improved among Veterans with care coordination compared with Veterans without care coordination using regression models and an appropriate matching  approach  for all proportions and means. Unadjusted comparisons between the two groups will be tested using approach . As an oapproach .For Aim 2, the main research question is “Are care coordination services associated with differences in perceived care integration and patient centeredness of care among Veterans with complex care needs? We hypothesize that Veterans with high-risk, high-need conditions with complex care needs who receive care coordination services will report greater levels of care integration and patient-centeredness than those who do not receive these services.We will assess Veterans’ perspectives about their experiences with care coordination, integration with other healthcare services, and perceived health impacts. We will sample and recruit participants from among Veterans identified in Aim 1 from FY2021 and based at VA sites with active care coordination programs (29 sites). Initially approved for a sample of 2,500 Veterans, we will invite participants from the selected sites and using a proportionate random sample of those with and without care coordination services.Measures will be drawn from an adaptation of Singer's Framework for Measuring Integrated Patient Care  and the staff knowledge about the patient’s medical history (3-item dimension), (2) provider support for the patient’s self-directed care (5-item dimension), (3) test result communication (3-item dimension), (4) provider knowledge of the patient (5-item dimension), (5) provider support for medication adherence and home health management (4-item dimension) and (6) specialist knowledge about the patient’s medical history (2-item dimension) (The PPIC 2.1 survey includes items organized under six dimensions of integrated patient care including: (1) mension) .Measures for analysis will be constructed from the six dimensions of the validated questionnaire items of the PPIC 2.1. Additional validated items will verify provider attribution, measure potential differences in individual tendency to give more positive or negative responses using the standardized life orientation test-revised (LOT-R) scale , and proTo account for clustering by VA site, we considered an intra-class correlation coefficient of 0.01. Previous work by Benzer et al.  using anIn descriptive analyses, we will compare mean scores for the six survey dimensions between Veterans with and without care coordination services. For individual survey items, we will also compare proportions of the sample of Veterans who reported the most positive rating (top-box score). Next, we plan to estimate six GLMMs, one for each of the six survey dimensions. We will compare mean dimension scores among Veterans with and without care coordination services, adjusting for Veterans’ sociodemographic and clinical characteristics, clustering at the VA facility level, and Veterans’ tendency to give more positive or negative responses using the LOT-R.For Aim 3, our research question is: “What are provider perceptions of care coordination needs assessment tools and process as they relate to concepts of innovation diffusion and care integration?” We expect that providers will highlight the importance of innovation attributes including relative advantage, compatibility, observability, and system innovation fit, and that perceptions about these factors will vary based on provider characteristics and roles.For Aim 3, we will gauge provider perspectives regarding care coordination tools and processes, and integration with care. We will conduct group and individual interviews with providers based at facilities where adoption of new care coordination processes and assessment tools are underway across the VA , 37. As Measures for group and individual interviews, which are based on diffusion of innovation theory and adapted from Pankratz et al.  perceiveFinally, informed by the qualitative interview data, we will develop and field a survey of VA providers. Measures will, again, be drawn from an adaptation of a scale for measuring perceptions of innovation adoption developed by Pankratz et al. . AdditioData from group and individual interviews will be analyzed using a rapid analysis approach and templated notes alongside iterative immersion. Results from this analysis will be used to give formative feedback to operational partners charged with implementing care coordination and needs assessments tools, and to refine the development of the broad provider survey. For the provider survey, we will use descriptive analyses and visualizations to understand survey item responses in relation to provider characteristics and roles, and facility characteristics such as size, patient volume, and type and number of PACT teams for primary and specialty care. Analyses will focus on identifying factors that are most predictive of perceptions of care coordination services and innovation affinity.This study will evaluate care coordination outcomes and processes in ongoing VA care for Veterans with complex care needs. In a prospective, observational, multiple methods study, we will (1) assess whether receipt of care coordination services impacts healthcare use, costs, and mortality among high-risk, high-need Veterans; (2) examine Veterans’ perspectives about care coordination and integration and whether receipt of care coordination services impacts these perceptions; and (3) explore provider perspectives about care coordination and integration as they relate to elements of innovation diffusion, innovation system fit, organizational power dynamics, and patient centeredness.The extent to which care coordination is contributing to outcomes among patients with complex care needs is not fully understood, and existing evidence is inconsistent regarding the design and effectiveness of tools to assess care coordination needs –12. In aStrengths of this study include our multidisciplinary research team consisting of clinical practitioners, organizational behavioral experts, implementation scientists, qualitative and quantitative methodologists, and clinical practice partnerships. Members of our research team also have experience working with a wide range of healthcare and related data for research and operations purposes.In addition, this research will be completed with insight from initiatives underway and led by VA clinical operations offices, in particular the VA Offices of Nursing Services (ONS), Care Management and Social Work (SW), and Integrated Veteran Care (IVC). These clinical operations offices are spearheading efforts to standardize and enhance care coordination practices across the VA, and have collaborated in prior reviews of care coordination research priorities , 49. As There are limitations to this study design. As an observational study, we will be limited to describing associations rather than causal effects. We also recognize that the CAN score, which is a well-validated tool for identifying one-year hospitalization and mortality risk, is an imperfect tool for assessing patient care needs. Like most health systems, the VA lacks standardized EHR-based assessment tools for care coordination and case management needs. We will include the robust clinical and social information that are available in the VA EHR, and therefore expect to demonstrate the importance of addressing information gaps and in consultation with our operational partners. In addition, although our Veteran survey will assess Veteran's perception of care integration, our study lacks qualitative analysis of Veteran's experiences, which could provide additional insight into whether Veterans are receiving the right care at the right time. Finally, while our study focuses on Veterans healthcare and may have limited generalizability, we may identify elements and processes that can be explored in other settings and populations.Indeed, our research dissemination process will be broad to motivate and inform further research and quality improvement. We have planned formal information sharing channels with VA stakeholders, including sharing survey results with participants and Veteran Engagement Groups. We will also share progress and early results with our clinical partners and other ongoing research initiatives through VA Health Services Research and Development venues. In addition, we will share our results and insights with the broader healthcare community. While our immediate goal of this research project is to inform development of an enhanced care coordination intervention to improve care integration and Veteran outcomes, we hope that our efforts can build evidence that may inform care coordination planning and implementation in other integrated health systems and community settings.By describing our approach and anticipated measures and analyses for this new study, we hope this information will be useful to researchers planning evaluations in other populations facing complex care needs. As we examine the relationship between care coordination processes and health outcomes, assess patient experiences, and gauge provider perspectives, we will share lessons learned. Ultimately, we expect our findings may inform others preparing to implement and evaluate care coordination programs in the VA and in other health systems.


Tosummarize recent findings in global mental health along several domains including socioeconomic determinants, inequities, funding, and inclusion in global mental health research and practice.Mental illness continues to disproportionately impact vulnerable populations and treatment coverage continues to be low globally. Advances in integrating mental health care and adopting task-shifting are accompanied by implementation challenges. The mental health impact of recent global events such as the COVID-19 pandemic, geo-political events, and environmental change is likely to persist and require coordinated care approaches for those in need of psychosocial support. Inequities also exist in funding for global mental health and there has been gradual progress in terms of building local capacity for mental health care programs and research. Lastly, there is an increasing effort to include people with lived experiences of mental health in research and policy shaping efforts.The field of global mental health will likely continue to be informed by evidence and perspectives originating increasingly from low- and middle-income countries along with ongoing global events and centering of relevant stakeholders.The online version contains supplementary material available at 10.1007/s11920-023-01426-8. The field of global mental health (GMH) aims to promote mental health, well-being, and treatment for populations across the world, centering transdisciplinary approaches and attainment of mental health equity and human rights •, 2. OveThis review examines recent advances in global mental health research along several broad domains—current burden of mental disorders, socioeconomic determinants of mental disorders, current priorities in global mental health, funding for global mental health, and progress towards centering important stakeholders in global mental health research and practice.Mental and substance use disorders are some of the leading causes of disability globally , 11. DepAccording to recent estimates, more than 13% of adolescents globally have a mental disorder, with common mental disorders such as anxiety and depressive disorders comprising about 40% of mental disorders . Mental Mental disorders not only debilitating but are also risk factors for fatal outcomes such as suicide and all-cause mortality –15. Litt16Social and economic determinants contribute to risk for mental disorders and disproportionately impact populations living in contexts of great adversity . A revieDiscrimination is one of the key Sustainable Development Goals  particularly in the context of recent events that have differentially affected members of racialized ethnic groups, sexual and gender minorities, stigmatized religious groups, and other marginalized communities. Multiple national surveys conducted in the USA, South Africa, and other countries have found that experiences of discrimination are associated with an increased odds of developing psychological disorders including depression, anxiety, psychotic disorders, and substance use disorders , 21. In Stigma and discrimination can differentially adversely affect people living with mental health conditions. The Lancet Commission on ending stigma and discrimination in mental health found mental health–related stigma can affect interpersonal relationships, career prospects, and discrimination in health care services received •. StigmaRecent research on risk factors has largely includes appraisals of aggregated evidence and the study of marginalization due to race and ethnicity experienced in high-income countries. More research is needed from other countries where social hierarchies manifest along lines of caste, community, religion, or other subgroups that may contribute to disparities in the onset and treatment of mental disorders.An important GMH priority has been to identify and implement appropriate treatment resources for populations in need of mental health care. Treatment approaches to address the continuum of mental health to mental illness range from self-care and informal support to community and facility-based mental health services including innovative digital interventions –29. HoweIntegrating mental health care into primary care settings has been emphasized for many years as an important mode of expanding mental health care access. The Collaborative Care Model (CoCM) developed at the University of Washington is a form of integrated care that treats common mental disorders such as depression or anxiety using a team-based approach trained primary care providers and embedded mental health specialists •, 31. ThPeople living with HIV (PLWH) are at an increased risk of experiencing mental disorders, and people with mental and substance use disorders are at greater risk of HIV acquisition •. When HTask-shifting (or task-sharing) uses abbreviated training of health care workers (HCWs) to enable redistribution of tasks for more efficient use of the human resources . It is fMost research on task-shifting was largely encouraging the use of task-shifting as a useful approach to expand mental health care access in low-resource settings. However, in recent years, research on task-shifting has now started to focus more on the nuances and challenges of this approach and is also gradually originating more from LMICs themselves than from HICs.Despite the high prevalence of mental health conditions worldwide, treatment coverage remains low and varies by setting and population. Furthermore, data on treatment coverage is not routinely collected despite it being part of international priorities such as the targets set by the World Health Organization Mental Health Action plan . There aProgress has been made towards expanding access to treatment using innovative and locally adapted care approaches . Many coThe prevention, diagnosis, and treatment of mental disorders among young people is a global priority . InterveWhile much progress has been made in addressing youth GMH, significant gaps remain in on the development of youth mental health interventions particularly in LMICs. Increased investment in mental health services and resources, greater emphasis on mental health literacy and education, enhanced collaboration across sectors, and the development and implementation of evidence-based interventions and treatments are needed to improve youth mental health globally .We review the literature on four key global events/phenomena that have exacerbated inequities in mental health, namely the current COVID-19 pandemic, political conflict and instability, human rights issues, and environmental events.The effect of the global COVID-19 pandemic on mental health has been well described in the literature . A recenInternational conflict and instability in the last few years have harmed the mental health of displaced populations. A study on the mental health effects of war on children due to the Russian invasion of Ukraine found that the cumulative impact of conflict puts estimated 7.5 million children at extreme mental and physical health risk , 71. AccHuman rights violations in mental health services are often underreported and underestimated . SeveralEnvironmental and climate change have impacted populations around the world not only due to catastrophic events, but gradual long-term changes as well. This can lead to stress and anxiety particularly for sub-populations that depend on natural resources/natural events for their economic sustenance and risk loss of livelihood and shelter due to environmental events , 87. ResRobust financing is needed to see strategies through to implementation •. CurrenHowever, progress has been made towards increasing local capacity building for research and sustained mental health services. The Global Alliance for Chronic Diseases consortium of funding agencies selected GMH for its annual call in 2017. A total of US$ 60 million of funding was made available through various participating funding agencies  with a focus on implementation research , 99. In It is also important to assess what strategies can be adopted to mobilize funds for GMH. An assessment of financing options for mental health in 6 Sub-Saharan showed that including mental health care as part of the national insurance system was an important strategy towards inclusive and equitable funding for mental health financing . A similGMH research needs to include and center relevant stakeholders including people with lived experiences of mental illness (PWLE) as their perspectives play a valuable role in shaping interventions and policies for mental health , 105. ThThere has been increasing attention on GMH methods in LMICs and decolonization of the field. Examples of inequities in research activities typically involve data collection efforts carried out by researchers from HICs in LMICs without acknowledging local research staff and knowledge in scientific publications or engaging in local capacity building—commonly termed “helicopter science” or “extractive science” . These iThis review highlights recent developments in the field of GMH in the past 3 years. Disparities in mental health conditions and access to adequate mental health care continue to exist globally and disproportionately affect socially disadvantaged populations Fig. . TreatmeSupplementary file1 (DOCX 15 KB)Below is the link to the electronic supplementary material.


This article was migrated. The article was marked as recommended.Longitudinal integrated clerkships (LICs) are a curricular structure for medical clerkships grounded in continuity across learning environments and experiences. There has yet to be a peer-reviewed article directly advising students in LIC programs. Twelve tips were created based on a comprehensive literature review of LICs and supported by the cumulative experience of the authors. They are ordered in four sequential groups: The first three tips discuss the importance of the relationships that are built between students and their patients (Tip 1), preceptors (Tip 2), and peers (Tip 3). Next we cover health systems, and offer advice on how students can integrate their learning (Tip 4), use technology to their advantage (Tip 5), and practice systems thinking (Tip 6). We then discuss the educational benefits when students take an active role in patient care (Tip 7), their own learning (Tip 8), and the feedback process (Tip 9). Finally, we cover the importance of self-care (Tip 10), reflection (Tip 11) and patience (Tip 12) during a LIC. These tips are designed to help students understand the pedagogical theory that underpins LICs, take an active role in their education, and maximize learning and wellness during their clerkship. The longitudinal integrated clerkship (LIC) is a model of clerkship training for medical students; as opposed to conventional “block-based” rotations, LICs are designed to provide continuity across learning environments and experiences. The LIC educational experience is therefore grounded in longitudinal relationships with preceptors, patients, peers, places, and pedagogy . In the Adoption of LICs has spread in part due to calls for curricular reform in medical education, particularly the need to train physicians for modern healthcare delivery systems and incorporate professional identity formation into medical training . As of MLiterature on LICs has increased exponentially over the last decade. LICs have been shown to increase students’ long-term learning . CurrentThese tips were developed iteratively by incorporating the available literature and the cumulative experience of the authors. Within each tip, we first address the pedagogical evidence and relevant learning theories before citing practical implementations for the LIC student. The tips are ordered in four sequential groups: The first three tips discuss the importance of the relationships that are built between students and their patients (Tip 1), preceptors (Tip 2), and peers (Tip 3). Next we cover health systems, and offer advice on how students can integrate their learning (Tip 4), use technology to their advantage (Tip 5), and practice systems thinking (Tip 6). We then discuss the educational benefits when students take an active role in patient care (Tip 7), their own learning (Tip 8), and the feedback process (Tip 9). Finally, we cover the importance of self-care (Tip 10), reflection (Tip 11) and patience (Tip 12) during a LIC.In LICs, students often form close relationships with their patients . Second,Seeing patients as “experts by experience” often requires a change in mindset on the part of students and preceptors  elderly patients, (2) patients with chronic pain, stroke, recent hospitalization, (3) patients with congenital conditions, autoimmune disease, or cancer, and (4) pregnant patients. Communicate with these patients and your providers about your role in the care team across departments.Students in LICs rely on technology to enhance their learning, communicate with teams, and track their cohort of patients throughout the year  and develop a list of relevant learning objectives. Use available online resources to research your learning objectives, add to your knowledge base, and test yourself on this material (see Tip 8). You may also choose to use podcasts, digital lectures, wikis, and online reference materials to enhance your learning on these subjects. Share useful resources with your peers and preceptors. Seek preceptor guidance to become facile with technologies in the clinic. Lastly, your post-clinic routine might include tracking cases you have seen in a digital logbook , a “written contract” of learning goals. ILPs require a learner to identify their learning needs, find resources to meet those needs, and evaluate their achievement. ILPs also help document self-assessment and self-directed learning as part of the practice-based learning and improvement competency . If you Medical school has been associated with high rates of burnout, anxiety, depression and alcohol abuse . Try to Develop your self-care practice. Exercising three times per week and nurturing relationships outside of medicine can be especially protective of mental health . Other eLastly, do not hesitate to seek support for anxiety, depression, feelings of isolation or suicidality or for substance abuse. Regrettably, these feeling remain more common among medical students than the general public, , developing proficiency with available technology (Tip 5), finding opportunities to integrate learning across settings and cases (Tip 4), and tracking and testing their learning over time (Tip 8). Such self-directed learning  allows slearning orientation rather thanperformance orientation. Practically, this is accomplished by developing trusting relationships with patients (Tips 1), preceptors (Tip 2), and peers (Tip 3), seeking out and incorporating longitudinal feedback (Tip 9), and critically reflecting on experiences (Tip 11).Second, these tips will also help students maintain a growth mindset . MedicalThird, these tips are intended to help student learners maintain perspective on their learning. By learning about health systems, LIC students can learn to function within a healthcare team and understand structural forces on their patients’ health (Tip 6). Perspective also helps students maintain balance during a stressful year (Tip 10) and be patient with the arc of learning that occurs during a LIC (Tip 12).The tips discussed in this paper will serve as scaffolding to succeed in your clerkship over time. As you gain knowledge, experience, and professional skills over the course of the year, you will add value to your patients  drive students’ learning.•Key skills in longitudinal clerkships for students include integrating learning across specialties, leveraging technology, and practicing systems thinking.•Students can enact practical strategies to promote self-directed learning, enhance direct patient care and incorporate feedback.•A longitudinal curricular structure provides opportunities for students to build skills of self-care, reflection, and patience.•As students progress through an LIC, they gain independence in their learning and clinical practice, develop a growth mindset, and maintain perspective on their learning within the context of health systems and their own educational journeys.Joshua Pepper is a fifth-year student in the UC Berkeley, UC San Francisco Joint Medical Program.Nardine Saad Riegels is an Associate Professor of Clinical Medicine, University of California San Francisco and Associate Professor of Health Systems Sciences, Kaiser Permanente School of Medicine. She serves as Clerkship Director for the Kaiser Permanente-UCSF Longitudinal Integrated Clerkship, East Bay and Director of Quality Programs for Graduate Medical Education at Kaiser Permanente East Bay.Tal Ann Ziv is an Associate Professor of Clinical Medicine, University of California San Francisco. She serves as Associate Director for the Kaiser Permanente-UCSF Longitudinal Integrated Clerkship, East Bay and Site Director, Internal Medicine Clerkship, University of California San Francisco, Kaiser Permanente East Bay.Lindsay Mazotti is an Associate Professor of Clinical Medicine, University of California San Francisco and Associate Professor of Clinical Sciences, Kaiser Permanente School of Medicine. She serves as Assistant Physician in Chief for Education and Development at Kaiser Permanente East Bay and Director of Clinical Experience for the Kaiser Permanente School of Medicine. This review has been migrated. The reviewer awarded 4 stars out of 5 This article addresses a gap in the literature regarding preparing an LIC student to maximize their experience. Though there is extensive research about LICs, there are few peer-reviewed articles specifically geared toward a student audience. The authors incorporate practical tips with comprehensive literature in a seamless manner. While some of the tips can be applied to any student entering their clinical rotations, much of the advice given is specific to the distinct aspects of LICs.I will plan to use this in our LIC program during orientation and will also share with other LIC programs.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 In contrast to traditional block clerkships, longitudinal integrated clerkships (LIC) provide continuity of relationships and learning environment. LICs have demonstrable benefits to learner, preceptor, patient and community. There is a growing literature on LICs, and this manuscript adds to the literature, by elegantly describing the ways that students can be agents in their learning. The grouping of the tips makes sense. The references are extensive and create a valuable bibliography for any and all interested in the topic. What stands out most, however, is that all of the tips could be and should be used by students in all clerkships, longitudinal or otherwise. It is a very well written manual for all students entering the clinical learning environment.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Longitudinal integrated clerkships provide a continuity of learning experiences and student feel more involved in the care of the patients. The twelve tips provided by the authors are grouped into those connected with relationships, health systems, educational benefits, and self-care, reflection and patience. With each tip the authors extensively cite the scientific literature before they provide practical advice to the student. The paper is well-written and makes for interesting reading. Longitudinal clerkships are becoming commoner and students and faculty members will find this article to be of interest.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Although a rather long paper to read, I enjoyed reading this paper and found the reference very extensive and useful. We are still learning about the true value of LICs but this paper covers the more practical approach to their construction. I particularly thought that the student and patient-centredness of the authors' twelve tips were a welcome approach to the subject. I would encourage all those involved with LICs to read this informative and practical paper.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 2 stars out of 5 The title "Twelve Tips for Students in Longitudinal Integrated Clerkships" raised my interest because of the LIC program. The paper focused on the common learning experience that a student may face in an LIC core-rotation. The paper is a good introduction to LIC for novice students entering their Clinical experience. It lacks the value and integrated learning advantages compare to the block-based clerkship.Reviewer Expertise:NANo decision status is available


Chronic wounds are associated with significant morbidity, marked loss of quality of life, and considerable economic burden. Evidence-based risk prediction to guide improved wound prevention and treatment is limited by the complexity in their etiology, clinical underreporting, and a lack of studies using large high-quality datasets.The objective of this review is to summarize key components and challenges in the development of personalized risk prediction tools for both prevention and management of chronic wounds, while highlighting several innovations in the development of better risk stratification.Regression-based risk prediction approaches remain important for assessment of prognosis and risk stratification in chronic wound management. Advances in statistical computing have boosted the development of several promising machine learning (ML) and other semiautomated classification tools. These methods may be better placed to handle large number of wound healing risk factors from large datasets, potentially resulting in better risk prediction when combined with conventional methods and clinical experience and expertise.Where the number of predictors is large and heterogenous, the correlations between various risk factors complex, and very large data sets are available, ML may prove a powerful adjuvant for risk stratifying patients predisposed to chronic wounds. Conventional regression-based approaches remain important, particularly where the number of predictors is relatively small. Translating estimated risk derived from ML algorithms into practical prediction tools for use in clinical practice remains challenging. Chronic wounds are associated with a diverse range of etiologies and complex interactions of risk factors. There is an urgent need for reliable, validated risk prediction tools that can better target an individual patient's unique set of predisposing factors that influence both their risk of disease progression and response to treatment once chronic wounds develop. The aim of this review is to summarize recent advancements in computing technologies to support risk prediction in other diseases and describe both the challenges in transferring these tools into clinical support for chronic wounds .Recent advancements in computing power and statistical risk prediction in cardiovascular and endocrine disease may be transferrable to complex, high-burden diseases such as chronic wounds. The growing availability of large clinical databases, disease registries, and administrative datasets provides a fertile base for the study and development of risk prediction tools for the targeted triaging and management of chronic wounds.Improved risk prediction is essential for the early identification of patients at highest risk for progressing to chronic wounds. This may enable early intervention in clinical practice to both prevent disease progression and support targeted, personalized management of disease once progressed, accounting for an individual patient's unique set of risk factors.1 Chronic wounds are associated with significant morbidity, marked loss of quality of life and utility, and considerable economic and societal burden, both within the United States and globally.2 Recent US estimates across all wound types put Medicare spending alone at USD 28–97 billion annually.3 Data from other countries further support the considerable costs of chronic wounds. Canadian expenditure on diabetic foot ulcers alone is estimated at $509 million annually,4 while chronic wound expenditure in the United Kingdom is estimated to be € 4.5–5.3 billion.5–8Complex wounds that fail to progress through expected healing phases in a timely manner are classified in the group of hard-to-heal wounds or chronic wounds.4 Current projections suggest that these costs will continue to increase into the future.A 2017 systematic review of 36 international cost-of-illness studies covering payer, hospital, patient, and societal perspectives estimated mean 1-year costs to the public payer ranging from $11,000 (USD) per chronic venous leg ulcer up to $44,200 for every diabetic foot ulcer.9 and a lack of quality prevalence studies, particularly at the global level.1 Critically, the timely identification, intervention, and personalized management of complex chronic wounds are further limited by a lack of reliable, validated risk prediction and prognostic tools. This is, in part, secondary to the complexity and range of comorbidities associated with chronic, often treatment-refractory wounds.7 These include such diverse chronic conditions as diabetes, chronic renal disease, venous insufficiency, peripheral vascular disease, and hypertension, in addition to pressure injuries secondary to lack of mobility and/or poor nutrition.Despite chronic wounds representing a major public health challenge, current cost estimates likely underestimate the true burden of disease due to underreporting,This complexity in etiology, variability in the underlying pathophysiology, and the attendant risk factors that each condition imparts are reflected in the large number of different guidelines currently available for the treatment and management of chronic wounds. The objective of this review is to summarize the key components and challenges in the development of personalized risk prediction tools for the prevention and management of chronic wounds, and highlight several promising innovations in the development of better risk stratification and prognosis tools, including machine learning (ML) and other broader artificial intelligence (AI)-based applications.10 The development and application of predictive tools for risk stratification may aid both targeted prevention of chronic wounds or improved, personalized treatment and management once wounds develop.11As knowledge and treatments for the management of chronic wounds evolve, treatment guidelines are increasingly emphasizing a more targeted approach that combines personalized medicine with evidence-based risk prediction for better patient-level and health care usage cost outcomes. Specifically, better tailoring of treatment to individual patients means a broader consideration of the factors that drive disease, including patient-related factors ; wound-related factors ; health care professional skills and knowledge; and resource treatment-related factors .9 Many conventional local therapies, growth factors, and dressing and biomaterial technologies for the management and treatment of chronic wounds remain generic. These interventions are commonly used in wound management irrespective of etiology or underlying risk factors that may impair wound healing. Moreover, the large number of available treatments for which evidence of effectiveness across the various phases of chronic wound disease is limited.Current standard of care for the management of chronic wounds covers multiple stages, including debridement, surgical drainage (where indicated), wound bed preparation, dressings and antimicrobial management of infection, and wound bioburden.13 These limitations extend to the economic evaluation of treatments for chronic wounds, both pharmaceutical and technological. A recent systematic review of model-based economic evaluations of venous leg ulcer treatments found that the reporting quality was generally low, particularly with regard to the reporting of evidence supporting the structure of each model used to translate the efficacy favoring a chronic wound intervention reported in the clinical trial setting into an economic cost-benefit or cost-utility.14This lack of validated, primary evidence is more pronounced in the biotechnological sphere, where promising technologies such as functional biomaterials and dressings are often insufficiently tested and trialled, making it difficult to establish causative associations between wound management technology and improved clinical and/or cost outcomes.11 Risk prediction may also need to appreciate systematic differences in health care delivery across different settings.15The heterogenous nature of chronic wounds makes personalized ulcer management challenging. A key component of personalized treatment of chronic wounds is better risk prediction. Being able to reliably predict which patients are likely to experience impaired ulcer healing would be a critical step in the tailoring of wound therapies to the individual patient and being able to respond in a timely manner when chronic wounds prove refractory to initial treatment. Predictive diagnostics have been identified as a critical component in the targeted prevention of various pathologies that characteristically drive poor wound healing.16 However, while analytical tools for the development of robust risk prediction for the prevention or targeted management of chronic wounds are evolving rapidly, their application in real-world clinical practice remains limited.Of the various etiologies or underlying disease that may present as chronic wounds, diabetes is one area that has seen recent advancements in personalized diabetes care. The development of risk prediction models that combine clinical and phenotypic data with biomarkers and genetic data to estimate individualized risk of diabetic complications have be used to risk-stratify and monitor patients to prevent or delay the development of such complications.17 As such, a critical component of any risk prediction tool is the availability of high-quality risk factor data. Risk factors can be divided into potentially modifiable risk factors  and nonmodifiable factors . Accessing a reasonably complete suite of risk factors required for reliable risk prediction in chronic wound can be challenging, given the large number of demographics, lifestyle, comorbidity, and other clinical factors that predispose to chronic wound disease or moderate their response to treatment.18–20In its simplest form, risk prediction models are mathematical equations that combine various patient-level risk factor data to estimate the probability of a future adverse event or poor clinical outcome, whether that be the initial development of a chronic wound or its subsequent response to therapy.21 high-quality real-world data such as disease registries are proving an increasingly valuable source of chronic wound risk factor data. These include high-coverage, national quality registries such as the Swedish National Quality Registry for Ulcer Treatment (RiksSår),22–24 and the Danish National Patient Register.25Despite so-called “big data” being recognized as generally lacking in wound healing outcome analysis,27Such large, national disease registries are typically characterized by excellent coverage and data quality and, unlike clinical trials, cover a broad spectrum of patient type and risk factor profiles that better characterize real-world clinical practice. Powering risk prediction tools on data collected from real-world clinical settings further improves the generalizability and utility of such tools to every-day clinical practice. The capacity to further link these registries to administrative data and electronic health records also provides opportunities to use novel methods such as ML and data mining to better identify relevant combinations of risk factors in the building of targeted, personalized risk prediction.28 ML and deep learning are the subsets of AI, and deep learning is a subset of ML , which are not synonyms, although frequently used interchangeably. From a very ambitious definition of “personalized medicine,” which often is described as “genomics-based knowledge that promises the ability to approach each patient as the biological individual he or she is,”35 They highlight the distinction between purely prognostic factors (that affect outcome irrespective of treatment) and those predictive of treatment response.36 However, recently introduced “patient-centric medicine” has a much broader scope defined as “individual's specific health needs and desired health outcomes” as “the driving force behind all health care decisions and quality measurements” in which “patients are partners with their health care providers, and providers treat patients not only from a clinical perspective, but also from an emotional, mental, spiritual, social, and financial perspective.”37The PROGRESS consortium defined stratified medicine as the targeting of treatments  according to the biological or clinical characteristics shared by subgroups of patients.The risk prediction models typically include clinical, genetic, and care-related factors and environmental, demographic, and other factors of importance for patient-related outcomes. Although judging based on the spectrum of factor, category, and outcome measures used in the risk prediction models, they can be described in certain situations as developments under the scope of patient-centric medicine. However, still, the majority of risk prediction models has a humbler approach and can be instead described under the scope of stratified/precision medicine. Even today, we lack a consistent analytical approach that can account for multiple patient attributes combined to inform treatment decisions adequately at the individual level.23 Current best practice guidelines highlight the importance of appropriate variable selection strategies, assessment of model performance, and external validation as three of the key steps in the development of good risk prediction.24 Variable selection, the identification of which combination of strong risk or prognostic factors best predicts the clinical outcome of interest, is a key determinant of both model performance and its ultimate utility in clinical or value-based payment settings. Although conventional selection methods generalized from linear regression analyses such as forward selection and backward elimination remain popular,25 such approaches have many disadvantages, such as instability of the selection and suboptimal model performance.Access to high-quality and representative data is just the first of several key phases in the development of clinically meaningful risk prediction models. Ideally, models balance statistical performance with clinical usefulness, achieving a satisfactory level of discrimination, calibration, and face validity.a priori evidence-based supporting of one variable over another is often limited. In the case of chronic wounds, the use of shrinkage of estimated regression coefficients, penalized likelihood, resampling, and stability testing may offer better alternatives to standard automated step-wise variable selection, particularly when combined with expert clinical opinion.26Moreover, these standard selection methods may be less practical for real-world risk prediction in chronic wound, where the number of potential candidate risk factors is typically large and the 27–29 External validation typically takes the form of assessing the performance of a risk prediction model in a separate dataset and can reveal key mismatches between a model's discrimination and calibration, which may reflect genuine differences in the cohort used to derive of the original prediction model and real-world settings.Model stability , calibration (agreement between the estimated and observed event risks), and discrimination  are key elements in assessing performance. These can be assessed at internal validation of a risk prediction model, that is, without access to new, independent data, for example, using bootstrapping techniques. External validation is central to establishing the model's generalizability to different or new subgroups of chronic wound patients.33 whereas an FRS-based risk prediction model of coronary artery disease (CAD) returned similar levels of discrimination in Asian and non-Asian cohorts; only on subsequent external validation was it apparent that the same model markedly overestimated the absolute risk of CAD, by 276% in men and 102% in women.34 Similarly, an American College of Cardiology/American Heart Association (ACC/AHA) risk model overestimated CAD risk by 75% to 150% in validation cohorts.34 These examples highlight the common problem of poor calibration of absolute risk predictions.32One well-publicized example involved the Framingham Risk Score (FRS), commonly used in risk prediction for cardiovascular endpoints,33 Similarly, electronic medical records (EMRs) are proving an increasing valuable resource for both developing and validating risk prediction in chronic wounds. A 2020 study of 620,356 chronic wounds (various etiologies) from the EMRs of 261,398 patients in the United States was able to predict wound healing with an area under the curve of 0.72,34 although this was less than the accuracy observed in other studies. Whatever the data source or stage of model development, it is essential that the future of risk prediction in chronic wounds follows best practice recommendations both in terms of model development35 and in reporting.36The emergence of large, high-quality national wound registries provides further opportunities to optimize the performance of chronic wound risk prediction by dividing these large databases into meaningful training and testing datasets, including by region or health care provider.37 Decision tree models are also well established in risk stratification and can be useful for both clinical outcome prediction and ranking the relative importance of competing risk factors to that prediction, may result in adequate risk prediction relative to conventional linear regression.What is broadly acknowledged is that risk prediction in chronic wounds is challenging, given the high level of heterogeneity in patient, disease, monitoring, treatment, and health care system factors that correlate with the various clinical outcomes. Conventional risk prediction models based on linear combinations of risk factors remain an important cornerstone of any attempt to quantify a patient's individual risk status.Risk prediction studies in chronic wound research employing conventional or algorithmic classification schemes generally tend to focus on three main outcomes: (1) wound healing; (2) in-hospital adverse events; and (3) wound management decision making . Characteristics of a selection of risk prediction studies for each of these outcomes are summarized in 38 This study highlights the importance of power and the availability of large datasets for driving reliable classification-based risk prediction. ML may not always outperform traditional approaches, and indeed may return relatively poor classification or prognostication, particularly where the training datasets are small.37 The very large number of risk factors at play, frequent interactions between competing risk factors, and nonlinear relationships between risk factors and, for example, delayed healing time, motivate the application of more innovative and sophisticated statistical solutions.39A U.S study of 620,356 chronic wounds (multiple etiologies) from 261,398 patients reported that a classification tree-based prediction model (AUC = 0.72) was broadly equivalent to conventional regression modeling (AUC = 0.71) in predicting wound healing within 12 weeks.40 Supervised learning algorithms  have been suggested to be associated with superior accuracy in identifying treatment responders in other diseases.41Promising new approaches include ML methods. ML employs automated statistical algorithms to identify and then learn to recognize patterns in data, for example, combinations of risk factors that optimally predict development of a chronic wound. Three basic classes of ML methods are commonly used in risk prediction: supervised, unsupervised, and reinforcement learning.39 The sheer volume of risk factors studied  coupled to the very large sample size of over 1 million wounds would have been logistically and computationally challenging. In another study, ML models based on Light Gradient Boosting Machine (LightGBM) algorithms could accurately classify the risk of major inpatient amputation (AUC 0.86) in a large, real-world cohort of patients with diabetic foot ulcers.42These techniques can be applied to large, real-world datasets such as patient registries, administrative datasets, and electronic health records to efficiently risk stratify patients based on their underlying risk factor profile. A recent 2021 study of 1,220,576 wounds from 425,163 patients, sourced from electronic health records in the United States, reported that ML-derived risk prediction models accurately predicted wounds at risk of not healing (AUC 0.86).43 Furthermore, an ML model trained on the electronic health records of 50,851 admissions to tertiary intensive care units outperformed the Braden score in predicting patients who subsequently developed chronic pressure ulcers.43 ML can also be used in decision support. Mombini et al applied ML to the analysis of a large volume of patient and image data of visual wound features to accurately predict treatment and referral decisions.44 More broadly, studies combining telemedicine with automated computational learning approaches for monitoring wound development and predicting patients in need of intervention or a change in treatment are currently underway.45ML models may be additionally useful as screening tools.47 A limitation of conventional approaches to risk prediction in chronic wounds is a patient's risk for, say delayed wound healing, is often assessed at a single point-in-time only . However, a chronic wound patient's real-world risk profile is likely to vary and fluctuate over time, depending upon how their underlying disease is being managed or otherwise. Multistate models allow a patient to move between varying risk states, and can result in better estimation of the true level risk associated with a particular factor.Another promising class of risk prediction methods is the use of multistate models to allow for dynamic risk prediction.46A critical aspect when making the leap from prognosis to a treatment recommendation is the causality. Risk prediction should thus form only one part of a decision support system to guide choices in the management of chronic wounds. The volume and heterogeneity of potential confounders of treatment response in chronic wound management make it difficult to isolate potentially causal pathways amenable to treatment, particularly when the risk prediction models are trained and tested in real-world datasets. This has, in part, formed an important driver of the increased popularity of ML in this field.47 and the prediction of lung adenocarcinoma through CT.48 The combination of such methods for data-driven causal hypothesis and future application of causal inference methods may bring personalized, targeted chronic wound management one step closer.49–51Although the application of deep-learning models for building confounder-invariant risk prediction in chronic wounds has thus far been limited, the method has recently shown promise in the use of MRIs in the diagnosis of HIV,43 This may, in part, reflect the recency and novelty of many of these ML approaches.Nevertheless, such novel ML methods should be taken with caution and should not be universally applied to any research question, despite demonstrated superiority in specific areas. A 2020 review of 453 articles published between 2015 and 2019 on ML predictive models for the diagnosis of chronic diseases noted the large variety of ML methods employed and the lack of standard methods for determining the optimal approach.17 Although ML methods are attractive, they are very data dependent. Those models can lack the interpretability of predictor models grounded in subject matter knowledge. Furthermore, the clinical utility of algorithmic predictions needs to consider the benefit-harm consequences for patients of false positive and false negatives. For example, a higher rate of false positives may be acceptable in real-world clinical practice if it avoids undue harm.51 The trade-off between these choices should therefore be balanced with practical clinical needs. ML may play an important role for identifying complex combinations and/or interactions between various risk factors if large data sets are available.Routine clinical practice requires simple, interpretable models of risk that use predictors that are easy to measure and not overly time-consuming.53 These measures consider the differential clinical consequences of false-positive versus true-positive classifications in a summary measure for clinical usefulness. ML-based risk prediction is not in itself sufficient to establish causal relationships between risk factors and wound outcomes and the general lack of standards or best practice in the development and application of the reviewed ML-based risk prediction has likely contributed to this lack of transparency and the relatively poor uptake of such technology in the clinical setting.However, the ultimate tool for use in real-world clinical practice needs to be pragmatic, intuitive, and actionable. Some performance measures have been proposed recently to quantify the ability of a prediction model to improve decision making.In terms of future work assessing the clinical usefulness of ML in wound management, this study group is currently undertaking a formal study applying ML methods to real-world data from the Swedish RiksSår quality registry for patients with difficult-to-heal wounds, to identify clusters of predictive factors that best predict wound class  in clinical practice.Conventional regression-based approach to risk prediction will remain important, particularly where the number of predictors is relatively small, and assumptions underlying the chosen model form such as linearity are carefully assessed. Furthermore, translating estimated risk derived from ML algorithms into practical, standalone prediction tools for use in everyday clinical practice  can be challenging. Where the number of predictors is large and heterogenous (as is characteristic of chronic wounds), the relationships and correlations between various risk factors are complex, and very large data sets are available, ML may prove a powerful adjuvant for risk-stratifying patients predisposed to or living with chronic wounds.Chronic wounds are associated with considerable morbidity, loss of quality of life. and significant economic burden globally.Reliable risk prediction is urgently needed for earlier identification and better management of disease.Recent advancements in computing power and statistical methods based on ML and analysis of big data show promise for the development of personalized risk stratification in the real-world management of chronic wounds.