Creating Local Learning Health Systems: Think Globally, Act Locally

Postoperative Global Period Cost Reduction Using 3 Successive Risk-Stratified Pancreatectomy Clinical Pathways

BACKGROUND

We hypothesized that iterative revisions of our original 2016 risk-stratified pancreatectomy clinical pathways would be associated with decreased 90-day perioperative costs.

STUDY DESIGN

From a single-institution retrospective cohort study of consecutive patients with 3 iterations: "version 1" (V1) (October 2016 to January 2019), V2 (February 2019 to October 2020), and V3 (November 2020 to February 2022), institutional data were aggregated using revenue codes and adjusted to constant 2022-dollar value. Grand total perioperative costs (primary endpoint) were the sum of pancreatectomy, inpatient care, readmission, and 90-day global outpatient care. Proprietary hospital-based costs were converted to ratios using the mean cost of all hospital operations as the denominator.

RESULTS

Of 814 patients, pathway V1 included 363, V2 229, and V3 222 patients. Accordion Grade 3+ complications decreased with each iteration (V1: 28.4%, V2: 22.7%, and V3: 15.3%). Median length of stay decreased (V1: 6 days, interquartile range [IQR] 5 to 8; V2: 5 [IQR 4 to 6]; and V3: 5 [IQR 4 to 6]) without an increase in readmissions. Ninety-day global perioperative costs decreased by 32% (V1 cost ratio 12.6, V2 10.9, and V3 8.6). Reduction of the index hospitalization cost was associated with the greatest savings (-31%: 9.4, 8.3, and 6.5). Outpatient care costs decreased consistently (1.58, 1.41, and 1.04). When combining readmission and all outpatient costs, total "postdischarge" costs decreased (3.17, 2.59, and 2.13). Component costs of the index hospitalization that were associated with the greatest savings were room or board costs (-55%: 1.74, 1.14, and 0.79) and pharmacy costs (-61%: 2.20, 1.61, and 0.87; all p < 0.001).

CONCLUSIONS

Three iterative risk-stratified pancreatectomy clinical pathway refinements were associated with a 32% global period cost savings, driven by reduced index hospitalization costs. This successful learning health system model could be externally validated at other institutions performing abdominal cancer surgery.

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records

OBJECTIVE

As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD.

MATERIALS AND METHODS

We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes.

RESULTS

Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008.

DISCUSSION

Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior.

CONCLUSION

Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

Health Care Bias and Discrimination Experienced by Lesbian, Gay, Bisexual, Transgender, and Queer Parents of Children With Developmental Disabilities: A Qualitative Inquiry in the United States

INTRODUCTION

This study explored the impact of health care (HC) bias and discrimination on lesbian, gay, bisexual, transgender, and queer (LGBTQ) parents and their children with disabilities in the United States, including the timing of developmental screening and diagnosis.

METHOD

We conducted semistructured interviews with 16 LGBTQ parents of children with developmental concerns or disabilities recruited through a prior national survey. Interviews were transcribed and analyzed using a combined inductive and deductive approach.

RESULTS

Discrimination types reported included noninclusive forms, disclosure challenges, and providers dismissing nongestational parents and diverse families. Few parents reported screening and diagnosis delays. Parents' recommendations included: avoiding assumptions, honoring family diversity, increasing LGBTQ family support, improving HC forms, increasing antibias training, and convening a learning community.

DISCUSSION

Our study advances the knowledge around HC bias and discrimination among LGBTQ parents of children with disabilities. Findings highlight the need for increased LGBTQ-affirming family support and research representing LGBTQ family diversity in U.S. health care.

Toward Population Health: Using a Learning Behavioral Health System and Measurement-Based Care to Improve Access, Care, Outcomes, and Disparities

Achieving population behavioral health is urgently needed. The mental health system struggles with enormous challenges of providing access to mental health services, improving quality and equitability of care, and ensuring good health outcomes across subpopulations. Little data exists about increasing access within highly constrained resources, staging/sequencing treatment along care pathways, or personalizing treatments. The conceptual model of the learning healthcare system offers a potential paradigm shift for addressing these challenges. In this article we present an overview of how the three constructs of population health, learning health systems, and measurement-based care are inter-related, and we provide an example of how one academic, community-based, safety net health system is approaching integrating these paradigms into its service delivery system. Implementation outcomes will be described in a subsequent publication. We close by discussing how ultimately, to meaningfully improve population behavioral health, a learning healthcare system could expand into a learning health community in order to target critical points of prevention and intervention.

Peripheral nerve surgery in Serbia: "Think global, act local" and the privilege of service

Introduction

The phrase "think globally, act locally", which has often been used to refer to conservation of the environment, highlights the importance of maintaining a holistic perspective and stipulates that each individual has a role to play in their community and larger world. Although peripheral nerve surgery has been largely unemphasized in global neurosurgical efforts, a wide disparity in peripheral nerve surgery is presumed to exist between high-income and low- and middle-income countries. Serbia is an upper middle-income country with a long history of peripheral nerve surgery.

Research question

How can understanding the development of peripheral nerve surgery in Serbia advance global education and improve peripheral nerve surgery worldwide?

Material and methods

An anecdotal and narrative review of recent advances in peripheral nerve surgery in Serbia was conducted. The World Federation of Neurosurgical Society (WFNS) Peripheral Nerve Surgery Committee discussions on improving peripheral nerve surgery education were summarized.

Results

In this manuscript, we describe the application of "think globally, act locally" to peripheral nerve surgery by providing an account of the development of peripheral nerve surgery in Serbia. Then, we report measures taken by the WFNS Peripheral Nerve Surgery Committee to improve education on peripheral nerve surgery in LMICs.

Discussion and conclusion

Viewing the development of peripheral nerve surgery in Serbia through the lens of "think globally, act locally" may guide the development of peripheral nerve surgery in LMICs.

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Leveraging a Safety Event Management System to Improve Organizational Learning and Safety Culture

BACKGROUND AND OBJECTIVES

Safety event management systems (SEMS) are rich sources of patient safety information, which can be used to improve organizational safety culture. An ideal SEMS can accomplish this when the system is improved with the intention of increasing learning and engagement across the organization. To support a global aim of improving overall patient safety and becoming a highly reliable learning health system, focus was directed toward increasing event review and follow-up completion and using this information to drive resource allocation and improvement efforts.

METHODS

A new integrated SEMS was customized, tested, and implemented based on identified organizational need. Revised policies were developed to define expectations for event review and follow-up. The new SEMS incorporated a closed-loop communication process which ensured information from events was shared with the event submitters and facilitated shared learning. The expected impacts, improved event reporting, and follow-up were studied and guided ongoing improvements.

RESULTS

After transitioning to a new SEMS, we experienced increased overall reporting by 8.6% and improved event follow-up, demonstrated by documentation on specified system forms, by 53.7%.

CONCLUSIONS

By implementing a new, efficient, and standardized SEMS, which decentralized event management processes, the organization saw increased reporting and better engagement with patient safety event review and follow-up. Overall, these results demonstrated a stronger reporting culture, which allowed for local problem solving and improved learning from every event reported. A robust reporting culture positively impacted the overall organizational culture of safety.

Collaborative conversations during the time of COVID-19: Building a "meta"-learning community

PROBLEM

COVID-19 created new research, clinical, educational, and personal challenges, while simultaneously separating work teams who were under work-from-home restrictions. Addressing these challenges required new forms of collaborative groups.

APPROACH

To support the department community and the rapid sharing of new research, educational, clinical, and personal efforts, a Core Team from the Department of Learning Health Sciences at the University of Michigan developed a meeting series called the COVID Conversations. This Experience Report shares the organizational structure of the COVID Conversations, proposes a comparison to traditional Learning Communities, and reports the results of a questionnaire that gathered details about department members' COVID-related activities.

OUTCOMES

We identify and describe salient similarities and differences between the COVID Conversations and the characteristics of Learning Communities. We also developed and piloted a taxonomy for characterizing LHS research projects that may be further developed for use in Learning Community planning, in conjunction with other maturity grids and ontologies. We propose the term "Meta-Learning Community" to describe the structure and function of the COVID Conversations.

NEXT STEPS

In academic medicine, remote work, telemedicine, and virtual learning may be here to stay. The COVID Conversations constitute a distinct and innovative form of collaborative work in which separate teams addressing distinct goals, yet sharing a common passion to tackle the issues brought by the pandemic, are able to share experiences and learn from one other. The challenges of COVID-19 have made evident the need for multiple forms of organizing teamwork, and our study contributes the notion of a "Meta"-Learning Community as a new form of collaborative work.

Embedding Learning in a Learning Health Care System to Improve Clinical Practice

PROBLEM

In an ideal learning health care system (LHS), clinicians learn from what they do and do what they learn, closing the evidence-to-practice gap. In operationalizing an LHS, great strides have been made in knowledge generation. Yet, considerable challenges remain to the broad uptake of identified best practices. To bridge the gap from generating actionable knowledge to applying that knowledge in clinical practice, and ultimately to improving outcomes, new information must be disseminated to and implemented by frontline clinicians. To date, the dissemination of this knowledge through traditional avenues has not achieved meaningful practice change quickly.

APPROACH

Vanderbilt University Medical Center (VUMC) developed QuizTime, a smartphone application learning platform, to provide a mechanism for embedding workplace-based clinician learning in the LHS. QuizTime leverages spaced education and retrieval-based practice to facilitate practice change. Beginning in January 2020, clinician-researchers and educators at VUMC designed a randomized, controlled trial to test whether the QuizTime learning system influenced clinician behavior in the context of recent evidence supporting the use of balanced crystalloids rather than saline for intravenous fluid management and new regulations around opioid prescribing.

OUTCOMES

Whether spaced education and retrieval-based practice influence clinician behavior and patient outcomes at the VUMC system level will be tested using the data currently being collected.

NEXT STEPS

These findings will inform future directions for developing and deploying learning approaches at scale in an LHS, with the goal of closing the evidence-to-practice gap.

Alliances to disseminate addiction prevention and treatment (ADAPT): A statewide learning health system to reduce substance use among justice-involved youth in rural communities

BACKGROUND

Youth in the justice system (YJS) are more likely than youth who have never been arrested to have mental health and substance use problems. However, a low percentage of YJS receive SUD services during their justice system involvement. The SUD care cascade can identify potential missed opportunities for treatment for YJS. Steps along the continuum of the cascade include identification of treatment need, referral to services, and treatment engagement. To address gaps in care for YJS, we will (1) implement a learning health system (LHS) to develop, or improve upon, alliances between juvenile justice (JJ) agencies and community mental health centers (CMHC) and (2) present local cascade data during continuous quality improvement cycles within the LHS alliances.

METHODS/DESIGN

ADAPT is a hybrid Type II effectiveness implementation trial. We will collaborate with JJ and CMHCs in eight Indiana counties. Application of the EPIS (exploration, preparation, implementation, and sustainment) framework will guide the implementation of the LHS alliances. The study team will review local cascade data quarterly with the alliances to identify gaps along the continuum. The study will collect self-report survey measures longitudinally at each site regarding readiness for change, implementation climate, organizational leadership, and program sustainability. The study will use the Stages of Implementation Completion (SIC) tool to assess the process of implementation across interventions. Additionally, the study team will conduct focus groups and qualitative interviews with JJ and CMHC personnel across the intervention period to assess for impact.

DISCUSSION

Findings have the potential to increase SUD need identification, referral to services, and treatment for YJS.

Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature

The “learning health system” (LHS) concept has been defined in broad terms, which makes it challenging for health system leaders to determine exactly what is required to transform their organization into an LHS. This study provides a conceptual map of the LHS landscape by identifying the activities, principles, tools, and conditions that LHS researchers have associated with the concept. Through a multi‐step screening process, two researchers identified 79 publications from PubMed (published before January 2020) that contained information relevant to the question, “What work is required of a healthcare organization that is operating as an LHS?” Those publications were coded as to whether or not they referenced each of 94 LHS elements in the taxonomy developed by the study team. This taxonomy, named the Learning Health Systems Consolidated Framework (LHS‐CF), organizes the elements into five “bodies of work” (organizational learning, translation of evidence into practice, building knowledge, analyzing clinical data, and engaging stakeholders) and four “enabling conditions” (workforce skilled for LHS work, data systems and informatics technology in place, organization invests resources in LHS work, and supportive organizational culture). We report the frequency that each of the 94 elements was referenced across the 79 publications. The four most referenced elements were: “organization builds knowledge or evidence,” “quality improvement practices are standard practice,” “patients and family members are actively engaged,” and “organizational culture emphasizes and supports learning.” By dissecting the LHS construct into its component elements, the LHS‐CF taxonomy can serve as a useful tool for LHS researchers and practitioners in defining the aspects of LHS they are addressing. By assessing how often each element is referenced in the literature, the study provides guidance to health system leaders as to how their organization needs to evolve in order to become an LHS ‐ while also recognizing that each organization should emphasize elements that are most aligned with their mission and goals.

Aiming for quality: a global compass for national learning systems

INTRODUCTION

Transforming a health system into a learning one is increasingly recognized as necessary to support the implementation of a national strategic direction on quality with a focus on frontline experience. The approach to a learning system that bridges the gap between practice and policy requires active exploration.

METHODS

This scoping review adapted the methodological framework for scoping studies from Arksey and O'Malley. The central research question focused on common themes for learning to improve the quality of health services at all levels of the national health system, from government policy to point-of-care delivery.

RESULTS

A total of 3507 records were screened, resulting in 101 articles on strategic learning across the health system: health professional level (19%), health organizational level (15%), subnational/national level (26%), multiple levels (35%), and global level (6%). Thirty-five of these articles focused on learning systems at multiple levels of the health system. A national learning system requires attention at the organizational, subnational, and national levels guided by the needs of patients, families, and the community. The compass of the national learning system is centred on four cross-cutting themes across the health system: alignment of priorities, systemwide collaboration, transparency and accountability, and knowledge sharing of real-world evidence generated at the point of care.

CONCLUSION

This paper proposes an approach for building a national learning system to improve the quality of health services. Future research is needed to validate the application of these guiding principles and make improvements based on the findings.

The Indiana Learning Health System Initiative: Early experience developing a collaborative, regional learning health system

INTRODUCTION

Learning health systems (LHSs) are usually created and maintained by single institutions or healthcare systems. The Indiana Learning Health System Initiative (ILHSI) is a new multi-institutional, collaborative regional LHS initiative led by the Regenstrief Institute (RI) and developed in partnership with five additional organizations: two Indiana-based health systems, two schools at Indiana University, and our state-wide health information exchange. We report our experiences and lessons learned during the initial 2-year phase of developing and implementing the ILHSI.

METHODS

The initial goals of the ILHSI were to instantiate the concept, establish partnerships, and perform LHS pilot projects to inform expansion. We established shared governance and technical capabilities, conducted a literature review-based and regional environmental scan, and convened key stakeholders to iteratively identify focus areas, and select and implement six initial joint projects.

RESULTS

The ILHSI successfully collaborated with its partner organizations to establish a foundational governance structure, set goals and strategies, and prioritize projects and training activities. We developed and deployed strategies to effectively use health system and regional HIE infrastructure and minimize information silos, a frequent challenge for multi-organizational LHSs. Successful projects were diverse and included deploying a Fast Healthcare Interoperability Standards (FHIR)-based tool across emergency departments state-wide, analyzing free-text elements of cross-hospital surveys, and developing models to provide clinical decision support based on clinical and social determinants of health. We also experienced organizational challenges, including changes in key leadership personnel and varying levels of engagement with health system partners, which impacted initial ILHSI efforts and structures. Reflecting on these early experiences, we identified lessons learned and next steps.

CONCLUSIONS

Multi-organizational LHSs can be challenging to develop but present the opportunity to leverage learning across multiple organizations and systems to benefit the general population. Attention to governance decisions, shared goal setting and monitoring, and careful selection of projects are important for early success.

Realizing a learning health system through process, rigor and culture change

While many healthcare organizations strive to achieve the patient care benefits of being a learning health system (LHS), myriad challenges stand in the way of successful implementation. The reality of creating a true LHS requires top-to-bottom commitment to culture change with the necessary vision, leadership, and investment. The Center for Outcomes Research and Evaluation (CORE) is a multidisciplinary research unit embedded within a large, vertically integrated healthcare system in the southeastern United States. We used a two-pronged approach to: a) methodically recruit a team of experts, while generating early wins that demonstrated real success; and b) build relationships and buy-in across organizational leadership. Building out a team with diverse expertise created the ability to deploy pragmatic, data-driven research designs that fit seamlessly into real-world care delivery, resulting in agile study execution that aligns with health system timelines. Case study examples from hospital readmissions and antibiotic stewardship illustrate how our LHS operationalizes practice-informed research and research-informed practice. Lessons from this experience can serve as a blueprint for other healthcare systems or networks seeking to expand the promise of the LHS framework to improve health for patients and communities.

Geisinger's effort to realize its potential as a learning health system: A progress report

OBJECTIVES

In the last two decades, several organizational initiatives have moved Geisinger into closer alignment with the key characteristics of the learning health system (LHS) model. The intent of this experience report is to provide a firsthand view of the potential of the model and of the complex, multifaceted nature of any endeavor designed and implemented to realize that potential.

METHODS

After describing Geisinger, we offer a critical self-assessment of our progress toward the goal of becoming an LHS, followed by an account of the challenges.

RESULTS

Geisinger has made incremental but measurable progress in implementing the LHS model, especially in two key domains: in patient-clinician engagement and science and informatics. Other challenges, however, present significant opportunities for additional forward movement, especially with respect to incentives, culture, and leadership.

CONCLUSION

Becoming a fully realized LHS is and will be a long-term challenge for any organization that embraces this aspiration.

Scaling Beyond Early Adopters: a Content Analysis of Literature and Key Informant Perspectives

BACKGROUND

Innovations and improvements in care delivery are often not spread across all settings that would benefit from their uptake. Scale-up and spread efforts are deliberate efforts to increase the impact of innovations successfully tested in pilot projects so as to benefit more people. The final stages of scale-up and spread initiatives must contend with reaching hard-to-engage sites.

OBJECTIVE

To describe the process of scale-up and spread initiatives, with a focus on hard-to-engage sites and strategies to approach them.

DESIGN

Qualitative content analysis of systematically identified literature and key informant interviews.

PARTICIPANTS

Leads from large magnitude scale-up and spread projects.

APPROACH

We conducted a systematic literature search on large magnitude scale-up and spread and interviews with eight project leads, who shared their perspectives on strategies to scale-up and spread clinical and administrative practices across healthcare systems, focusing on hard-to-engage sites. We synthesized these data using content analysis.

KEY RESULTS

Searches identified 1919 titles, of which 52 articles were included. Thirty-four discussed general scale-up and spread strategies, 11 described hard-to-engage sites, and 7 discussed strategies for hard-to-engage sites. These included publications were combined with interview findings to describe a fourth phase of the national scale-up and spread process, common challenges for spreading to hard-to-engage sites, and potential benefits of working with hard-to-engage sites, as well as useful strategies for working with hard-to-engage sites.

CONCLUSIONS

We identified scant published evidence that describes strategies for reaching hard-to-engage sites. The sparse data we identified aligned with key informant accounts. Future work could focus on better documentation of the later stages of spread efforts, including specific tailoring of approaches and strategies used with hard-to-engage sites. Spread efforts should include a "flexible, tailored approach" for this highly variable group, especially as implementation science is looking to expand its impact in routine care settings.

Building, scaling, and sustaining a learning health system for surgical quality improvement: A toolkit

This article describes how to start, replicate, scale, and sustain a learning health system for quality improvement, based on the experience of the Michigan Surgical Quality Collaborative (MSQC). The key components to operationalize a successful collaborative improvement infrastructure and the features of a learning health system are explained. This information is designed to guide others who desire to implement quality improvement interventions across a regional network of hospitals using a collaborative approach. A toolkit is provided (under Supporting Information) with practical information for implementation.

Engineering Approaches for Addressing Opioid Use Disorder in the Community

Many communities in the United States are struggling to deal with the negative consequences of illicit opioid use. Effectively addressing this epidemic requires the coordination and support of community stakeholders in a change process with common goals and objectives, continuous engagement with individuals with opioid use disorder (OUD) through their treatment and recovery journeys, application of systems engineering principles to drive process change and sustain it, and use of a formal evaluation process to support a learning community that continuously adapts. This review presents strategies to improve OUD treatment and recovery with a focus on engineering approaches grounded in systems thinking.

Hospital participation in clinical trials for patients with acute myocardial infarction: Results from the National Cardiovascular Data Registry

BACKGROUND

Little is known about the proportion of hospitals in the United States that offer clinical trial enrollment opportunities and how patient outcomes differ between hospitals that do and do not participate in clinical trials.

METHODS

In the nationwide Chest Pain-MI registry, we described the proportion of hospitals that enrolled patients with acute myocardial infarction (MI) in clinical trials from 2009 to 2014. Hospital-level adherence to every eligible MI performance measure was compared between hospitals that did and did not enroll patients in clinical trials. Using linked Medicare data, we also compared 1-year major adverse cardiovascular events (MACE: death, MI, heart failure, or stroke) among patients ≥65 years old treated at trial versus nontrial hospitals.

RESULTS

Among 766 hospitals, 430 (56.1%) enrolled ≥1 MI patient in a clinical trial during the study period, but the proportion of hospitals enrolling patients in clinical trials declined from 36.8% in 2009 to 26.6% in 2014. Complete adherence to performance measures was delivered to a greater proportion of patients at trial hospitals than nontrial hospitals (72.6% vs 64.9%, P < .001; adjusted OR 1.07, 95% CI 1.03-1.12). One-year MACE rates were also lower for trial hospitals (adjusted HR 0.96, 95% CI 0.93-0.99).

CONCLUSIONS

Hospitals are becoming less likely to engage in clinical trials for patients with MI. Patients admitted to hospitals that participated in clinical trials more often received guideline-adherent care and had better long-term outcomes.

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and populations.

Research IT maturity models for academic health centers: Early development and initial evaluation

This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.

"Learn From Every Patient": How a Learning Health System Can Improve Patient Care

Aim:

We created a Learning Health System, the “Learn From Every Patient” program, embedded in our cerebral palsy team clinic. This program was designed to simultaneously provide clinical care while systematically collecting data for quality improvement and research projects on all patients.

Method:

Clinicians created tools within the Electronic Health Record to discretely capture data for clinical use which was also available for quality improvement/research efforts. At baseline, all patients in our clinic received annual hip x-rays to screen for hip displacement. Using our “Learn From Every Patient” database, we reviewed the outcomes for the most mildly affected patients, Level I on the Gross Motor Functional Classification System.

Results:

One hundred thirty-two patients were classified as Gross Motor Functional Classification System Level I. During the study period, these patients received 212 pelvis x-rays, viewing 424 hips, of which 419 (98.8%) were normal. Five hips (1.2%) had < 30% displacement. None had any hip-related symptoms nor required any procedures during the period. We used these data to create an evidence-based change in our standardized hip screening procedure by eliminating annual screening x-rays for this population.

Interpretation:

This implementation of a local learning health system approach to systematically collect research data simultaneously with routine clinical care enabled us to implement an evidence-based improvement in clinical practice. This complete integration of research into clinical care improved care by reducing radiation exposure, while simultaneously reducing health care costs.

A structured bowel management program for patients with severe functional constipation can help decrease emergency department visits, hospital admissions, and healthcare costs

BACKGROUND

Published health-care costs related to constipation in children in the USA are estimated at $3.9 billion/year. We sought to assess the effect of a bowel management program (BMP) on health-care utilization and costs.

METHODS

At two collaborating centers, BMP involves an outpatient week during which a treatment plan is implemented and objective assessment of stool burden is performed with daily radiography. We reviewed all patients with severe functional constipation who participated in the program from March 2011 to June 2015 in center 1 and from April 2014 to April 2016 in center 2. ED visits, hospital admissions, and constipation-related morbidities (abdominal pain, fecal impaction, urinary retention, urinary tract infections) 12 months before and 12 months after completion of the BMP were recorded.

RESULTS

One hundred eighty-four patients were included (center 1 = 96, center 2 = 88). Sixty-three (34.2%) patients had at least one unplanned visit to the ED before treatment. ED visits decreased to 23 (12.5%) or by 64% (p < 0.0005). Unplanned hospital admissions decreased from 65 to 28, i.e., a 56.9% reduction (p < 0.0005).

CONCLUSION

In children with severe functional constipation, a structured BMP decreases unplanned visits to the ED, hospital admissions, and costs for constipation-related health care.

LEVEL OF EVIDENCE

3.

IDEA4PS: The Development of a Research-Oriented Learning Healthcare System

Leveraging opportunities to learn and then improve the delivery of care using experiences throughout the health care system is essential in efforts to transform health care delivery. The authors present the approach of one academic medical center in becoming a research-oriented Learning Healthcare System (ro-LHS). By reframing the role of research in improving outcomes, the organization was able to move beyond its focus on quality improvement to foster a culture in which feedback informs practice and research drives improvement. The patient safety learning laboratory, the Institute for the Design of Environments Aligned for Patient Safety, funded by the Agency for Healthcare Research and Quality, has provided foundational infrastructure to connect stakeholders across the medical center and university and conduct rigorous research in the context of practice. With this new focus, research now informs operations in a cycle of continuous improvement across the authors' ro-LHS.

The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations

PURPOSE

The progress of the Millennium Development Goals (MDGs) shows that sustained global action can achieve success. Despite the unprecedented achievements in health and education, more than one billion people, many of them in conflict-affected areas, were unable to reap the benefits of the MDG gains. The recently developed Sustainable Development Goals (SDGs) are even more ambitious then their predecessor. SDG 3 prioritizes health and well-being for all ages in specific areas such as maternal mortality, communicable diseases, mental health, and healthcare workforce. However, without a shift in the approach used for conflict-affected areas, the world's most vulnerable people risk being left behind in global development yet again. We must engage in meaningful discussions about employing innovative strategies to address health challenges fragile, low-resource, and often remote settings. In this paper, we will argue that to meet the ambitious health goals of SDG 3, digital health can help to bridge healthcare gaps in conflict-affected areas.

METHODS

First, we describe the health needs of populations in conflict-affected environments, and how they overlap with the SDG 3 targets. Secondly, we discuss how digital health can address the unique needs of conflict-affected areas. Finally, we evaluate the various challenges in deploying digital technologies in fragile environments, and discuss potential policy solutions.

DISCUSSION

Persons in conflict-affected areas may benefit from the diffusive nature of digital health tools. Innovations using cellular technology or cloud-based solutions overcome physical barriers. Additionally, many of the targets of SDG 3 could see significant progress if efficacious education and outreach efforts were supported, and digital health in the form of mHealth and telehealth offers a relatively low-resource platform for these initiatives. Lastly, lack of data collection, especially in conflict-affected or otherwise fragile states, was one of the primary limitations of the MDGs. Greater investment in data collection efforts, supported by digital health technologies, is necessary if SDG 3 targets are to be measured and progress assessed. Standardized EMR systems as well as context-specific data warehousing efforts will assist in collecting and managing accurate data. Stakeholders such as patients, providers, and NGOs, must be proactive and collaborative in their efforts for continuous progress toward SDG 3. Digital health can assist in these inter-organizational communication efforts.

CONCLUSION

The SDGS are complex, ambitious, and comprehensive; even in the most stable environments, achieving full completion towards every goal will be difficult, and in conflict-affected environments, this challenge is much greater. By engaging in a collaborative framework and using the appropriate digital health tools, we can support humanitarian efforts to realize sustained progress in SDG 3 outcomes.

Clinical Note Creation, Binning, and Artificial Intelligence

The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.