Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.

OBJECTIVES: To examine the feasibility of promoting engagement with data-driven self-management of health among individuals from minoritized medically underserved communities by tailoring the design of self-management interventions to individuals' type of motivation and regulation in accordance with the Self-Determination Theory. METHODS: Fifty-three individuals with type 2 diabetes from an impoverished minority community were randomly assigned to four different versions of an mHealth app for data-driven self-management with the focus on nutrition, Platano; each version was tailored to a specific type of motivation and regulation within the SDT self-determination continuum. These versions included financial rewards (external regulation), feedback from expert registered dietitians (RDF, introjected regulation), self-assessment of attainment of one's nutritional goals (SA, identified regulation), and personalized meal-time nutrition decision support with post-meal blood glucose forecasts (FORC, integrated regulation). We used qualitative interviews to examine interaction between participants' experiences with the app and their motivation type (internal-external). RESULTS: As hypothesized, we found a clear interaction between the type of motivation and Platano features that users responded to and benefited from. For example, those with more internal motivation reported more positive experience with SA and FORC than those with more external motivation. However, we also found that Platano features that aimed to specifically address the needs of individuals with external regulation did not create the desired experience. We attribute this to a mismatch in emphasis on informational versus emotional support, particularly evident in RDF. In addition, we found that for participants recruited from an economically disadvantaged community, internal factors, such as motivation and regulation, interacted with external factors, most notably with limited health literacy and limited access to resources. CONCLUSIONS: The study suggests feasibility of using SDT to tailor design of mHealth interventions for promoting data-driven self-management to individuals' motivation and regulation. However, further research is needed to better align design solutions with different levels of self-determination continuum, to incorporate stronger emphasis on emotional support for individuals with external regulation, and to address unique needs and challenges of underserved communities, with particular attention to limited health literacy and access to resources.

Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation.

[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].

Design and Evaluation Challenges of Conversational Agents in Health Care and Well-being: Selective Review Study.

BACKGROUND: Health care and well-being are 2 main interconnected application areas of conversational agents (CAs). There is a significant increase in research, development, and commercial implementations in this area. In parallel to the increasing interest, new challenges in designing and evaluating CAs have emerged. OBJECTIVE: This study aims to identify key design, development, and evaluation challenges of CAs in health care and well-being research. The focus is on the very recent projects with their emerging challenges. METHODS: A review study was conducted with 17 invited studies, most of which were presented at the ACM (Association for Computing Machinery) CHI 2020 conference workshop on CAs for health and well-being. Eligibility criteria required the studies to involve a CA applied to a health or well-being project (ongoing or recently finished). The participating studies were asked to report on their projects' design and evaluation challenges. We used thematic analysis to review the studies. RESULTS: The findings include a range of topics from primary care to caring for older adults to health coaching. We identified 4 major themes: (1) Domain Information and Integration, (2) User-System Interaction and Partnership, (3) Evaluation, and (4) Conversational Competence. CONCLUSIONS: CAs proved their worth during the pandemic as health screening tools, and are expected to stay to further support various health care domains, especially personal health care. Growth in investment in CAs also shows the value as a personal assistant. Our study shows that while some challenges are shared with other CA application areas, safety and privacy remain the major challenges in the health care and well-being domains. An increased level of collaboration across different institutions and entities may be a promising direction to address some of the major challenges that otherwise would be too complex to be addressed by the projects with their limited scope and budget.

Enabling personalized decision support with patient-generated data and attributable components.

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems.

Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes.

Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals' engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants' attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one's life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual's proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive-relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals' engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.

BACKGROUND: Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE: This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS: A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS: The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS: Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual's GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

Personalized glucose forecasting for type 2 diabetes using data assimilation.

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Driven to distraction: The nature and apparent purpose of interruptions in critical care and implications for HIT.

OBJECTIVES: To examine the apparent purpose of interruptions in a Pediatric Intensive Care Unit and opportunities to reduce their burden with informatics solutions. MATERIALS AND METHODS: In this prospective observational study, researchers shadowed clinicians in the unit for one hour at a time, recording all interruptions participating clinicians experienced or initiated, their starting time, duration, and a short description that could help to infer their apparent purpose. All captured interruptions were classified inductively on their source and apparent purpose and on the optimal representational media for fulfilling their apparent purpose. RESULTS: The researchers observed thirty-four one-hour sessions with clinicians in the unit, including 21 nurses and 13 residents and house physicians. The physicians were interrupted on average 11.9 times per hour and interrupted others 8.8 times per hour. Nurses were interrupted 8.6 times per hour and interrupted others 5.1 times per hour. The apparent purpose of interruptions included Information Seeking and Sharing (n=259, 46.3%), Directives and Requests (n=70, 12%), Shared Decision-Making (n=49, 8.8%), Direct Patient Care (n=36, 6.4%), Social (n=71, 12.7%), Device Alarms (n=28, 5%), and Non-Clinical (n=10, 1.8%); 6.6% were not classified due to insufficient description. Of all captured interruptions, 29.5% were classified as being better served with informational displays or computer-mediated communication. CONCLUSIONS: Deeper understanding of the purpose of interruptions in critical care can help to distinguish between interruptions that require face-to-face conversation and those that can be eliminated with informatics solutions. The proposed taxonomy of interruptions and representational analysis can be used to further advance the science of interruptions in clinical care.

Impact of an electronic handoff documentation tool on team shared mental models in pediatric critical care.

OBJECTIVE: To examine the impact of the implementation of an electronic handoff tool (the Handoff Tool) on shared mental models (SMM) within patient care teams as measured by content overlap and discrepancies in verbal handoff presentations given by different clinicians caring for the same patient. MATERIALS AND METHODS: Researchers observed, recorded, and transcribed verbal handoffs given by different members of patient care teams in a pediatric intensive care unit. The transcripts were qualitatively coded and analyzed for content overlap scores and the number of discrepancies in handoffs of different team members before and after the implementation of the tool. RESULTS: Content overlap scores did not change post-implementation. The average number of discrepancies nearly doubled following the implementation (from 0.76 discrepancies per handoff group pre-implementation to 1.17 discrepancies per handoff group post-implementation); however, this change was not statistically significant (p=0.37). Discrepancies classified as related to dosage of treatment or procedure and to patients' symptoms increased in frequency post-implementation. DISCUSSION: The results suggest that the Handoff Tool did not have the desired positive impact on SMM within patient care teams. Future electronic tools for facilitating team handoff may need longer implementation times, complementary changes to handoff process and structure, and improved designs that integrate a common core of shared information with discipline-specific records. CONCLUSION: While electronic handoff tools provide great opportunities to improve communication and facilitate the formation of shared mental models within patient care teams, further work is necessary to realize their full potential.

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data.

OBJECTIVE: To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. MATERIALS AND METHODS: We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). RESULTS: The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals' activities and changes in their blood glucose levels that the participants referred to as "cause and effect". This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). DISCUSSION: The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes. CONCLUSIONS: Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions.

Revealing structures in narratives: A mixed-methods approach to studying interdisciplinary handoff in critical care.

OBJECTIVE: To examine a novel mixed-methods approach for studying patterns of clinical communication that could inform future informatics solutions, with a specific focus on handoff within interdisciplinary teams. MATERIALS AND METHODS: Researchers observed, recorded, and transcribed verbal handoff discussions of different members of critical care teams. The transcripts were coded qualitatively, and then analyzed quantitatively for emerging structural patterns using categorical cluster analysis, and for degree of shared mental models (SMM) using the modified Pyramid method. RESULTS: An empirical study using the proposed mixed-methods approach suggested emerging patterns of communication among clinicians. For example, the temporal focus of handoff was often determined by the role of the clinician giving the handoff; the clinical content of handoff was consistent between clinicians, but varied between patients. The SMM index ranged from 0.065 (with the maximum possible overlap score of 1) to 0.007 with a median of 0.026; the overlap was higher in statements concerned with patient presentation (23.6% of these had overlap) and referring to the past (24% overlapped). This calculated SMM index was correlated with the assessment of coherence within the participating teams by independent physicians (r=0.63, p=0.038). CONCLUSIONS: The proposed novel mixed-methods approach helped to reveal emerging patterns in content and structure of handoff communication and highlight differences due to the clinical context, and to the different priorities of clinicians on interdisciplinary patient care teams. The approach for calculating SMM is more ecologically sensitive as it relies on naturally occurring discourse and less intrusive than traditional ways of assessing SMM, and takes initial steps toward establishing empirical foundation for the design of electronic tools to support handoff in interdisciplinary teams.

Adopting the sensemaking perspective for chronic disease self-management.

BACKGROUND: Self-monitoring is an integral component of many chronic diseases; however few theoretical frameworks address how individuals understand self-monitoring data and use it to guide self-management. PURPOSE: To articulate a theoretical framework of sensemaking in diabetes self-management that integrates existing scholarship with empirical data. METHODS: The proposed framework is grounded in theories of sensemaking adopted from organizational behavior, education, and human-computer interaction. To empirically validate the framework the researchers reviewed and analyzed reports on qualitative studies of diabetes self-management practices published in peer-reviewed journals from 2000 to 2015. RESULTS: The proposed framework distinguishes between sensemaking and habitual modes of self-management and identifies three essential sensemaking activities: perception of new information related to health and wellness, development of inferences that inform selection of actions, and carrying out daily activities in response to new information. The analysis of qualitative findings from 50 published reports provided ample empirical evidence for the proposed framework; however, it also identified a number of barriers to engaging in sensemaking in diabetes self-management. CONCLUSIONS: The proposed framework suggests new directions for research in diabetes self-management and for design of new informatics interventions for data-driven self-management.

Factors influencing home health care providers' performance of oral health care for older people: A qualitative study.

AIM: To examine factors that affect the performance of oral health care (OHC) for older people receiving nursing care at home. BACKGROUND: Oral health is often neglected by health care providers caring for older people. Research shows that health care providers' provision of OHC may be influenced by various factors (barriers and facilitators). When this research was conducted, health care providers from home healthcare services (HHCS) and nursing homes were grouped together despite setting differences; therefore, this study focuses on the performance of OHC by home health care providers (HHCPs) as a single group. DESIGN: Explorative design with a qualitative approach. METHODS: The managers of four HHCS units recruited 17 HHCPs to participate in focus group interviews. One interview was conducted per unit, and there were four to five participants in each interview. The analysis of interviews was based on theoretical thematic analysis and the PRECEDE constructs in the PRECEDE-PROCEED model. Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines were used in reporting this study. FINDINGS: The analysis resulted in two themes with predisposing factors (HHCPs' professional responsibilities, older people's attitude), five themes with enabling factors (knowledge and skills, older people/carer trust, available time, available equipment and collaboration with public dental service (PDS)), and two themes with reinforcing factors (routines and OHC focus on the workplace) that affect the provision of OHC. The factors were categorised as individual, organisational and collaboration factors. CONCLUSIONS: In addition to individual factors found in previous studies, factors related to the organisation of services and communication between HHCPs and PDS seem to affect HHCPs' provision of OHC for adults receiving HHCS. IMPLICATIONS FOR PRACTICE: This study provides in-depth knowledge that can contribute to increasing HHCPs' provision of OHC and thereby prevent oral and dental disease among older people receiving HHCS.

In search of common ground in handoff documentation in an Intensive Care Unit.

OBJECTIVE: Handoff is an intra-disciplinary process, yet the flow of critical handoff information spans multiple disciplines. Understanding this information flow is important for the development of computer-based tools that supports the communication and coordination of patient care in a multi-disciplinary and highly specialized critical care setting. We aimed to understand the structure, functionality, and content of nurses' and physicians' handoff artifacts. DESIGN: We analyzed 22 nurses' and physicians' handoff artifacts from a Cardiothoracic Intensive Care Unit (CTICU) at a large urban medical center. We combined artifact analysis with semantic coding based on our published Interdisciplinary Handoff Information Coding (IHIC) framework for a novel two-step data analysis approach. RESULTS: We found a high degree of structure and overlap in the content of nursing and physician artifacts. Our findings demonstrated a non-technical, yet sophisticated, system with a high degree of structure for the organization and communication of patient data that functions to coordinate the work of multiple disciplines in a highly specialized unit of patient care. LIMITATIONS: This study took place in one CTICU. Further work is needed to determine the generalizability of the results. CONCLUSIONS: Our findings indicate that the development of semi-structured patient-centered interdisciplinary handoff tools with discipline specific views customized for specialty settings may effectively support handoff communication and patient safety.

Examining AI Methods for Micro-Coaching Dialogs.

Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition - brief coaching conversations related to specific meals, to support achievement of nutrition goals - and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.

Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease.

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.

Automated vs. Human Health Coaching: Exploring Participant and Practitioner Experiences.

Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

An investigation of the information technology needs associated with delivering chronic disease care to large clinical populations.

BACKGROUND: The growing number of individuals with complex medical and social needs has motivated the adoption of care management (CM) - programs wherein multidisciplinary teams coordinate and monitor the clinical and non-clinical aspects of care for patients with chronic disease. Despite claims that health information technology (IT) is essential to CM, there has been limited research focused on the IT needs of clinicians providing care management to large groups of patients with chronic disease. OBJECTIVE: To assess clinicians' needs pertaining to CM and to identify inefficiencies and bottlenecks associated with the delivery of CM to large groups of patients with chronic disease. METHODS: A qualitative study of two HIV care programs. Methods included observations of multidisciplinary care team meetings and semi-structured interviews with physicians, care managers, and social workers. Thematic analysis was conducted to analyze the data. RESULTS: CM was perceived by staff as requiring the development of novel strategies including patient prioritization and patient monitoring, which was supported by patient registries but also required the creation of additional homegrown tools. Common challenges included: limited ability to identify pertinent patient information, specifically in regards to social and behavioral determinants of health, limited assistance in matching patients to appropriate interventions, and limited support for communication within multidisciplinary care teams. CONCLUSION: Clinicians delivering care management to chronic disease patients are not adequately supported by electronic health records and patient registries. Tools that better enable population monitoring, facilitate communication between providers, and help address psychosocial barriers to treatment could enable more effective care.