Utilizing a learning health system to capture real-world patient data: Application of the reliable change index to evaluate and improve the outcome of a pain rehabilitation program

BACKGROUND AND OBJECTIVES

The learning healthcare system (LHS) has been developed to integrate patients' clinical data into clinical decisions and improve treatment outcomes. Having little guidance on this integration process, we aim to explain (a) an applicable analytic tool for clinicians to evaluate the clinical outcomes at a group and an individual level and (b) our quality improvement (QI) project, analyzing the outcomes of a new outpatient pain rehabilitation program ("Back-in-Action": BIA) and applying the analysis results to modify our clinical practice.

METHODS

Through our LHS (CHOIR; https://choir.stanford.edu), we administered the Pain Catastrophizing Scale (PCS), Chronic Pain Acceptance Questionnaire (CPAQ), and Patient-Reported Outcomes Measures (PROMIS)® before and after BIA. After searching for appropriate analytic tools, we decided to use the Reliable Change Index (RCI) to determine if an observed change in the direction of better (improvement) or worse (deterioration) would be beyond or within the measurement error (no change).

RESULTS

Our RCI calculations revealed that at least a 9-point decrease in the PCS scores and 10-point increase in the CPAQ scores would indicate reliable improvement. RCIs for the PROMIS measures ranged from 5 to 8 T-score points (i.e., 0.5-0.8 SD). When evaluating change scores of the PCS, CPAQ, and PROMIS measures, we found that 94% of patients showed improvement in at least one domain after BIA and 6% showed no reliable improvement.

CONCLUSIONS

Our QI project revealed RCI as a useful tool to evaluate treatment outcomes at a group and an individual level, and RCI could be incorporated into the LHS to generate a progress report automatically for clinicians. We further explained how clinicians could use RCI results to modify a clinical practice, to improve the outcomes of a pain program, and to develop individualized care plans. Lastly, we suggested future research areas to improve the LHS application in pain practice.

Which factors influenced the adoption of interprofessionality in health based on the reports of the PET-Health Interprofessionality projects in Brazil? A document analysis

The Program of Education through Work - Health (PET-Health) Interprofessionality is one of the strategic actions of the "Plan for the Strengthening of Interprofessionality" in healthcare in Brazil. Based on the experience of the program, this paperexamines the aspects that impact the adoption and strengthening of interprofessional education and collaborative practices, and issues recommendations for the strengthening of interprofessionality as a guiding principle of training and working in healthcare. This is a document analysis of partial reports from the six- and 12-months of execution of 120 PET-Health Interprofessionality projects in Brazil. The data were analyzed based on content analysis and the categories elaborated a priori. The aspects that impact the adoption and strengthening of interprofessionality in training and working in healthcare, and future recommendations, were organized in the relational, processual, organizational, and contextual dimensions, according to the framework by Reeves et al. The PET-Health Interprofessionality expanded the understanding of elements of interprofessional education and practice and showed that the discussion must take on a more political, critical, and reflexive character. The analysis points to the need for continuity of teaching-learning activities as a strategy to foster interprofessional capacity in healthcare services and consequent strengthening of the Unified Healthcare System in Brazil.

Learning healthcare systems in cardiology: A qualitative interview study on ethical dilemmas of a learning healthcare system

Background

Implementation of an LHS in cardiology departments presents itself with ethical challenges, including ethical review and informed consent. In this qualitative study, we investigated stakeholders' attitudes toward ethical issues regarding the implementation of an LHS in the cardiology department.

Methods

We conducted a qualitative study using 35 semi-structured interviews and 5 focus group interviews with 34 individuals. We interviewed cardiologists, research nurses, cardiovascular patients, ethicists, health lawyers, epidemiologists/statisticians and insurance spokespersons.

Results

Respondents identified different ethical obstacles for the implementation of an LHS within the cardiology department. These obstacles were mainly on ethical oversight in LHSs; in particular, informed con sent and data ownership were discussed. In addition, respondents reported on the role of patients in LHS. Respondents described the LHS as a possibility for patients to engage in both research and care. While the LHS can promote patient engagement, patients might also be reduced to their data and are therefore at risk, according to respondents.

Conclusions

Views on the ethical dilemmas of a LHSs within cardiology are diverse. Similar to the literary debate on oversight, there are different views on how ethical oversight should be regulated. This study adds to the literary debate on oversight by highlighting that patients wish to be informed about the learning activities within the LHS they participate in, and that they wish to actively contribute by sharing their data and identifying learning goals, provided that informed consent is obtained.

Learning health system benefits: Development and initial validation of a framework

Introduction

Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers. Engaging these stakeholders requires communicating clear, tangible value propositions. Existing models identify broad categories of benefits but do not explicate the full range of benefits or ways they can manifest in different organizations.

Methods

To develop such a framework, a working group with representatives from six Clinical and Translational Science Award (CTSA) hubs reviewed existing literature on LHS characteristics, models, and goals; solicited expert input; and applied the framework to their local LHS experiences.

Results

The Framework of LHS Benefits includes six categories of benefits (quality, safety, equity, patient satisfaction, reputation, and value) relevant for a range of stakeholders and defines key concepts within each benefit. Applying the framework to five LHS case examples indicated preliminary face validity across varied LHS approaches and revealed three dimensions in which the framework is relevant: defining goals of individual LHS projects, facilitating collaboration based on shared values, and establishing guiding tenets of an LHS program or mission.

Conclusion

The framework can be used to communicate the value of an LHS to different stakeholders across varied contexts and purposes, and to identify future organizational priorities. Further validation will contribute to the framework's evolution and support its potential to inform the development of tools to evaluate LHS impact.

The University of Alabama at Birmingham COVID-19 Collaborative Outcomes Research Enterprise: Developing an institutional learning health system in response to the global pandemic

Introduction

As a local response to the COVID‐19 global pandemic, the University of Alabama at Birmingham (UAB) established the UAB COVID‐19 Collaborative Outcomes Research Enterprise (CORE), an institutional learning health system (LHS) to achieve an integrated health services outcomes and research response.

Methods

We developed a network of expertise and capabilities to rapidly develop and deploy an institutional‐level interdisciplinary LHS. Based upon a scoping review of the literature and the Knowledge to Action Framework, we adopted a LHS framework identifying contributors and components necessary to developing a system within and between the university academic and medical centers. We used social network analysis to examine the emergence of informal work patterns and diversified network capabilities based on the LHS framework.

Results

This experience report details three principal characteristics of the UAB COVID‐19 CORE LHS development: (a) identifying network contributors and components; (b) building the institutional network; and (c) diversifying network capabilities. Contributors and committees were identified from seven components of LHS: (a) collaborative and executive leadership committee, (b) research coordinating committee, (c) oversight and ethics committee, (d) thematic scientific working groups, (e) programmatic working groups, (f) informatics capabilities, and (g) patient advisory groups. Evolving from the topical interests of the initial CORE participants, scientific working groups emerged to support the learning system network. Programmatic working groups were charged with developing a comprehensive and mutually accessible COVID‐19 database.

Discussion

Our LHS framework allowed for effective integration of multiple academic and medical centers into a cohesive institutional‐level learning system. Network analysis indicated diversity of institutional disciplines, professional rank, and topical focus pertaining to COVID‐19, with each center leveraging existing institutional responsibilities to minimize gaps in network capabilities.

Conclusion

Incorporating an adapted LHS framework designed for academic medical centers served as a foundational resource supporting further institutional‐level efforts to develop agile and responsive learning networks.

What can a learning healthcare system teach us about improving outcomes?

PURPOSE OF REVIEW

This review describes the learning healthcare system paradigm, recent examples, and future directions. Patients, clinicians, and health systems frequently encounter decisions between available treatments, technologies, and healthcare delivery methods with little or no evidence about the comparative effectiveness and safety of the available options. Learning healthcare systems endeavor to recognize such knowledge gaps, integrate comparative effectiveness research - including clinical trials - into clinical care to address the knowledge gaps, and seamlessly implement the results into practice to improve care and patient outcomes.

RECENT FINDINGS

Recent studies comparing the effectiveness of diagnostic tests and treatments, using information technology to identify patients likely to experience an outcome or benefit from an intervention, and evaluating models of healthcare delivery have demonstrated how a learning healthcare system approach can reduce arbitrary variation in care, decrease cost, and improve patient outcomes.

SUMMARY

Learning healthcare systems have the potential to answer questions of importance to patients, clinicians, and health system leaders, improve efficiency of healthcare delivery, and improve patient outcomes. Achieving this goal will require realignment of the culture around clinical care, institutional and federal investment, expanded stakeholder engagement, tailored ethical and regulatory guidance, and methodologic advances in information technology and biostatistics.

Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment

A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.

Adapting Coordinated Specialty Care in the Post-COVID-19 Era: Study Protocol for an Integrative Mixed-methods Study

Background : Coordinated Specialty Care (CSC) programs provide evidence-based services for young people with a recent onset of a psychotic disorder. OnTrackNY is a nationally recognized model of CSC treatment in New York state. In 2019, OnTrackNY was awarded a hub within the Early Psychosis Intervention Network (EPINET) to advance its learning health care system (LHS). The OnTrackNY network is comprised of 23 CSC teams across New York state. OnTrack Central, an intermediary organization, provides training and implementation support to OnTrackNY teams. OnTrack Central coordinates a centralized data collection protocol for quality improvement and evaluation of program fidelity and a mechanism to support practice based-research. OnTrackNY sites’ breadth coupled with OnTrack Central oversight provides an opportunity to examine the impacts of the COVID-19 crisis in New York State. Methods : This project will examine the implications of modifications to service delivery within the OnTrackNY LHS during and after the COVID-19 crisis. We will use the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) to classify systematically, code, and analyze modifications to CSC services and ascertain their impact. We will utilize integrative mixed methods. Qualitative interviews with multi-level stakeholders (program participants, families, providers, team leaders, agency leaders, trainers (OnTrack Central), and decision-makers at the state and local levels) will be used to understand the process making decisions, information about modifications to CSC services, and their impact. Analysis of OnTrackNY program data will facilitate examining trends in team staffing and functioning, and participant service utilization and outcomes. Study findings will be summarized in a CSC Model Adaptation Guide , which will identify modifications as fidelity consistent or not, and their impact on service utilization and care outcomes. Discussion : A CSC Model Adaptation Guide will inform CSC programs, and the state and local mental health authorities to which they are accountable, regarding modifications to CSC services and the impact of these changes on care process, and participant service utilization and outcomes. The guide will also inform the development of tailored technical assistance that CSC programs may need within OnTrackNY, the EPINET network, and CSC programs nationally. Trial Registration : NCT04021719, July 16 th , 2019.

Multicenter data to improve health for pediatric renal transplant recipients in North America: Complementary approaches of NAPRTCS and IROC

Kidney transplantation increases life expectancy and improves quality of life for children with end-stage kidney disease, yet sequelae of transplantation and treatment make it difficult for transplant recipients to enjoy health and quality of life similar to their healthy peers. The NAPRTCS network was among the first to use multicenter data to inform improvements in care and outcomes for children with a kidney transplant through observational research. Now, with new technologies and unprecedented access to data, it is possible to create learning health systems as envisioned by the US National Academy of Sciences to seamlessly integrate research and continuous improvement of clinical care. In this review, we present two pre-eminent North American networks focused on using multicenter data to drive improved care and outcomes for children with a kidney transplant. Whereas, for the past 30 years NAPRTCS has focused on discovery of best practices through observational research and clinical trials, the Improving Renal Outcomes Collaborative, established in 2016, engages patients, families, clinicians, and researchers in redesigning the healthcare delivery system to enable practice change and continuous improvement of health outcomes. We discuss the history and past contributions of these networks, as well as current activities, barriers, and potential future solutions to more fully realize the vision of a true learning health system for pediatric kidney transplant recipients.

Seeding Structures for a Community of Practice Focused on Transient Ischemic Attack (TIA): Implementing Across Disciplines and Waves

BACKGROUND

The Community of Practice (CoP) model represents one approach to address knowledge management to support effective implementation of best practices.

OBJECTIVE

We sought to identify CoP developmental strategies within the context of a national quality improvement project focused on improving the quality for patients receiving acute transient ischemic attack (TIA) care.

DESIGN

Stepped wedge trial.

PARTICIPANTS

Multidisciplinary staff at six Veterans Affairs medical facilities.

INTERVENTIONS

To encourage site implementation of a multi-component quality improvement intervention, the trial included strategies to improve the development of a CoP: site kickoff meetings, CoP conference calls, and an interactive website (the "Hub").

APPROACH

Mixed-methods evaluation included data collected through a CoP attendance log; semi-structured interviews with site participants at 6 months (n = 32) and 12 months (n = 30), and CoP call facilitators (n = 2); and 22 CoP call debriefings.

KEY RESULTS

The critical seeding structures that supported the cultivation of the CoP were the kickoffs which fostered relationships (key to the community element of CoPs) and provided the evidence base relevant to TIA care (key to the domain element of CoPs). The Hub provided the forum for sharing quality improvement plans and other tools which were further highlighted during the CoP calls (key to the practice element of CoPs). CoP calls were curated to create a positive context around participants' work by recognizing team successes. In addition to improving care at their local facilities, the community created a shared set of tools which built on their collective knowledge and could be shared within and outside the group.

CONCLUSIONS

The PREVENT CoP advanced the mission of the learning healthcare system by successfully providing a forum for shared learning. The CoP was grown through seeding structures that included kickoffs, CoP calls, and the Hub. A CoP expands upon the learning collaborative implementation strategy as an effective implementation practice.

Implementation of a learning healthcare system for sickle cell disease

Objective

Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time.

Materials and Methods

Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed.

Results

The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence.

Conclusions

Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

Evaluating a cardiovascular disease risk management care continuum within a learning healthcare system: a prospective cohort study

BACKGROUND

Many patients now present with multimorbidity and chronicity of disease. This means that multidisciplinary management in a care continuum, integrating primary care and hospital care services, is needed to ensure high quality care.

AIM

To evaluate cardiovascular risk management (CVRM) via linkage of health data sources, as an example of a multidisciplinary continuum within a learning healthcare system (LHS).

DESIGN & SETTING

In this prospective cohort study, data were linked from the Utrecht Cardiovascular Cohort (UCC) to the Julius General Practitioners' Network (JGPN) database. UCC offers structured CVRM at referral to the University Medical Centre (UMC) Utrecht. JGPN consists of electronic health record (EHR) data from referring GPs.

METHOD

The cardiovascular risk factors were extracted for each patient 13 months before referral (JGPN), at UCC inclusion, and during 12 months follow-up (JGPN). The following areas were assessed: registration of risk factors; detection of risk factor(s) requiring treatment at UCC; communication of risk factors and actionable suggestions from the specialist to the GP; and change of management during follow-up.

RESULTS

In 52% of patients, ≥1 risk factors were registered (that is, extractable from structured fields within routine care health records) before UCC. In 12%-72% of patients, risk factor(s) existed that required (change or start of) treatment at UCC inclusion. Specialist communication included the complete risk profile in 67% of letters, but lacked actionable suggestions in 86%. In 29% of patients, at least one risk factor was registered after UCC. Change in management in GP records was seen in 21%-58% of them.

CONCLUSION

Evaluation of a multidisciplinary LHS is possible via linkage of health data sources. Efforts have to be made to improve registration in primary care, as well as communication on findings and actionable suggestions for follow-up to bridge the gap in the CVRM continuum.

OnTrackNY's learning healthcare system

Worldwide, early intervention services for young people with recent-onset psychosis have been associated with improvements in outcomes, including reductions in hospitalization, symptoms, and improvements in treatment engagement and work/school participation. States have received federal mental health block grant funding to implement team-based, multi-element, evidence-based early intervention services, now called coordinated specialty care (CSC) in the USA. New York State's CSC program, OnTrackNY, has grown into a 23-site, statewide network, serving over 1800 individuals since its 2013 inception. A state-supported intermediary organization, OnTrackCentral, has overseen the growth of OnTrackNY. OnTrackNY has been committed to quality improvement since its inception. In 2019, OnTrackNY was awarded a regional hub within the National Institute of Mental Health-sponsored Early Psychosis Intervention Network (EPINET). The participation in the national EPINET initiative reframes and expands OnTrackNY's quality improvement activities. The national EPINET initiative aims to develop a learning healthcare system (LHS); OnTrackNY's participation will facilitate the development of infrastructure, including a systematic approach to facilitating stakeholder input and enhancing the data and informatics infrastructure to promote quality improvement. Additionally, this infrastructure will support practice-based research to improve care. The investment of the EPINET network to build regional and national LHSs will accelerate innovations to improve quality of care.

A tale of three subspecialties: Diagnosis recording patterns are internally consistent but Specialty-Dependent

BACKGROUND

Structured diagnosis (DX) are crucial for secondary use of electronic health record (EHR) data. However, they are often suboptimally recorded. Our previous work showed initial evidence of variable DX recording patterns in oncology charts even after biopsy records are available.

OBJECTIVE

We verified this finding's internal and external validity. We hypothesized that this recording pattern would be preserved in a larger cohort of patients for the same disease. We also hypothesized that this effect would vary across subspecialties.

METHODS

We extracted DX data from EHRs of patients treated for brain, lung, and pancreatic neoplasms, identified through clinician-led chart reviews. We used statistical methods (i.e., binomial and mixed model regressions) to test our hypotheses.

RESULTS

We found variable recording patterns in brain neoplasm DX (i.e., larger number of distinct DX-OR = 2.2, P < 0.0001, higher descriptive specificity scores-OR = 1.4, P < 0.0001-and much higher entropy after the BX-OR = 3.8 P = 0.004 and OR = 8.0, P < 0.0001), confirming our initial findings. We also found strikingly different patterns for lung and pancreas DX. Although both seemed to have much lower DX sequence entropy after the BX-OR = 0.198, P = 0.015 and OR = 0.099, P = 0.015, respectively compared to OR = 3.8 P = 0.004). We also found statistically significant differences between the brain dataset and both the lung (P < 0.0001) and pancreas (0.009

CONCLUSION

Our results suggest that disease-specific DX entry patterns exist and are established differently by clinical subspecialty. These differences should be accounted for during clinical data reuse and data quality assessments but also during EHR entry system design to maximize accurate, precise and consistent data entry likelihood.

Collapse

Key Words

• clinical data management
• data quality
• electronic health records
• learning healthcare system
• secondary use of clinical data

Collapse

MESH Headings Collapse

Grants

• K12 GM102773 NIGMS NIH HHS
• UL1 TR001420 NCATS NIH HHS

Collapse

Affiliation(s)

Jose-Franck Diaz-Garelli Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Roy Strowd Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Tamjeed Ahmed Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Brian J Wells Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Rebecca Merrill Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Javier Laurini Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Boris Pasche Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA
Umit Topaloglu Wake Forest Baptist Medical Center, Winston Salem, North Carolina, USA

Collapse

Evaluation protocol of the implementation of a learning healthcare system in clinical practice: the Connected Health Cities programme in the north of England

INTRODUCTION

The 'learning healthcare system' (LHS) has been proposed to deliver better outcomes for patients and communities by analysing routinely captured health information and feeding back results to clinical staff. This approach is being piloted in the Connected Health Cities (CHC) programme in four regions in the north of England. This article describes the protocol of the evaluation of this programme.

METHODS AND ANALYSIS

In designing this evaluation, we had to take a pragmatic approach to ensure the feasibility of completing the work within 1 year. Furthermore, we have designed the evaluation in such a way as to be able to capture differences in how each of the CHC regions uses a variety of methods to create their own LHS. A mixed methods approach has been adopted for this evaluation due the scale and complexities of the pilot study. A documentary review will identify how CHC pilot study deliverables were operationalised. To gain a broad understanding of CHC staff experiences, an online survey will be offered to all staff to complete. Semi-structured interviews with key programme staff will be used to gain a deeper understanding of key achievements, as well as how challenges have been overcome or managed. Our data analysis will triangulate the documentary review, survey and interview data. A thematic analysis using our logic model as a framework will also be used to assess progress against the CHC programme deliverables and to identify recommendations to support future programme decision-making.

ETHICS AND DISSEMINATION

Ethical approval was granted by The University of Manchester Ethics Committee on 24 May 2018. The results will be actively disseminated through peer-reviewed journals, conference presentations, social media, the internet and various stakeholder/patient and public engagement activities.

A Model for Genome-First Care: Returning Secondary Genomic Findings to Participants and Their Healthcare Providers in a Large Research Cohort

There is growing interest in communicating clinically relevant DNA sequence findings to research participants who join projects with a primary research goal other than the clinical return of such results. Since Geisinger's MyCode Community Health Initiative (MyCode) was launched in 2007, more than 200,000 participants have been broadly consented for discovery research. In 2013 the MyCode consent was amended to include a secondary analysis of research genomic sequences that allows for delivery of clinical results. Since May 2015, pathogenic and likely pathogenic variants from a set list of genes associated with monogenic conditions have prompted "genome-first" clinical encounters. The encounters are described as genome-first because they are identified independent of any clinical parameters. This article (1) details our process for generating clinical results from research data, delivering results to participants and providers, facilitating condition-specific clinical evaluations, and promoting cascade testing of relatives, and (2) summarizes early results and participant uptake. We report on 542 participants who had results uploaded to the electronic health record as of February 1, 2018 and 291 unique clinical providers notified with one or more participant results. Of these 542 participants, 515 (95.0%) were reached to disclose their results and 27 (5.0%) were lost to follow-up. We describe an exportable model for delivery of clinical care through secondary use of research data. In addition, subject and provider participation data from the initial phase of these efforts can inform other institutions planning similar programs.

Enhancing diversity to reduce health information disparities and build an evidence base for genomic medicine

Advances in genomic medicine are arising from efforts to build a national learning healthcare system (LHS) and large-scale precision medicine studies. However, the underlying evidence base lacks sufficient data from populations historically underrepresented in biomedical research. Although the literature on health and healthcare disparities is extensive, disparities in the availability and quality of health information about diverse and underrepresented populations are less well characterized. This Perspective describes scientific and ethical benefits to incorporating health information from diverse and underrepresented populations in the LHS, resulting in a more robust and generalizable LHS. Near-term recommendations for incorporating diversity into the evidence base for genomic medicine are proposed, even as the groundwork for national and international efforts is underway.

Clinically feasible stratification of 1-year to 3-year post-myocardial infarction risk

Objective

Post-myocardial infarction (MI) care is crucial to preventing recurrent major adverse cardiovascular events (MACE), but can be complicated to personalise. A tool is needed that effectively stratifies risk of cardiovascular (CV) events 1–3 years after MI but is also clinically usable.

Methods

Patients surviving ≥1 year after an index MI with ≥1 risk factor for recurrent MI (ie, age ≥65 years, prior MI, multivessel coronary disease, diabetes, glomerular filtration rate <60 mL/min/1.73 m²) were studied. Cox regression derived sex-specific Intermountain Major Adverse Cardiovascular Events (IMACE) risk scores for the composite of 1-year to 3-year MACE (CV death, MI or stroke). Derivation was performed in 70% of subjects (n=1342 women; 3047 men), with validation in the other 30% (n=576 women; 1290 men). Secondary validations were also performed.

Results

In women, predictors of CV events were glucose, creatinine, haemoglobin, platelet count, red cell distribution width (RDW), age and B-type natriuretic peptide (BNP); among men, they were potassium, glucose, blood urea nitrogen, haematocrit, white blood cell count, RDW, mean platelet volume, age and BNP. In the primary validation, in women, IMACE ranged from 0 to 11 (maximum possible: 12) and had HR=1.44 per +1 score (95% CI 1.29 to 1.61; P<0.001); men had IMACE range 0–14 (maximum: 16) and HR=1.29 per +1 score (95% CI 1.20 to 1.38; P<0.001). IMACE ≥5 in women (≥6 in men) showed strikingly higher MACE risk.

Conclusions

Sex-specific risk scores strongly stratified 1-year to 3-year post-MI MACE risk. IMACE is an inexpensive, dynamic, electronically delivered tool for evaluating and better managing post-MI patient care.

Transparency and choice in learning healthcare systems

Learning healthcare systems rely on potentially sensitive data and biospecimens from patients who typically have no knowledge of secondary uses of these resources for research. While this failure to inform patients of these practices is consistent with human subject regulations for research, these practices risk controversy and a loss of trust in the integrity of healthcare institutions. This article reviews recent controversies in this domain and argues for new institutional practices that entail patient education about secondary uses of data and biospecimens and the opportunity for patient choice in the form of an opt-out system. This approach would enhance transparency and reduce the risk of a loss of public trust in the research enterprise.

Detecting Risk of Low Health Literacy in Disadvantaged Populations Using Area-based Measures

Introduction

Socio-economic status (SES) and low health literacy (LHL) are closely correlated. Both are directly associated with clinical and behavioral risk factors and healthcare outcomes. Learning healthcare systems are introducing small-area measures to address the challenges associated with maintaining patient-reported measures of SES and LHL. This study's purpose was to measure the association between two available census block measures associated with SES and LHL. Understanding the relationship can guide the identification of a multi-purpose area based measure for delivery system use.

Methods

A retrospective observational design was deployed using all US Census block groups in Utah. The principal dependent variable was a nationally-standardized health literacy score (HLS). The primary explanatory variable was a state-standardized area deprivation index (ADI). Statistical methods included linear regression and tests of association. Receiver operating characteristic (ROC) analysis was used to develop LHL criteria using ADI.

Results

A significant negative association between the HLS and the ADI score remained after adjusting for area-level risk factors (β: -0.21 (95% CI: -0.22, -0.19) p < .001). Eighteen block groups (<1%) were identified as having LHL using HLS. A combination of three or more ADI components correlated with LHL predicted 78% of HLS LHL block groups and 35 additional block groups not identified using HLS (c-statistic: 0.64; 95% CI: 0.62, 0.66).

Conclusions

HLS and ADI use differing measurement criteria but are closely correlated. A state-based ADI detected additional neighborhoods with risk of LHL compared to use of a national HLS. An ADI represents a multi-purpose area measure of social determinants useful for learning health systems tailoring care.

Digital maturity: are we ready to use technology in the NHS?

Digital maturity assessments (DMAs) are a self-assessment mechanism for organisations. They can be effectively utilised to generate local digital roadmaps. In their simplest form, these allow organisations to understand their state of readiness to integrate digital technologies. This is achieved by assessing the capability and compatibility of their information systems to communicate or interface both within and across organisations. Through utilising and responding to the findings of DMAs, it is thought that the NHS will be better able to provide a patient-centred service to meet local needs within a national framework. It is this exchange and integration of information across health and social care systems that will drive innovation and transformation in the NHS.

Pharmacotherapy within a learning healthcare system: rationale for the Dutch Santeon Farmadatabase

Objectives

The increasing number of available, often expensive, medicines asks for continuous assessment of rational prescribing. We aimed to develop a simple and robust data infrastructure in order to monitor hospital medicine utilisation in real time.

Methods

Within a collaboration (Santeon) of large teaching hospitals in the Netherlands, we set up a process for extraction, transformation, anonymisation and load of individual medicine prescription data and major clinical outcomes from different hospital information systems into a central database. Quarterly reports were constructed to monitor and validate the quality of the uploaded data.

Results

A central database has been developed that includes data from all patients from 2010 onwards and is refreshed on a weekly basis by an automated process. Beginning in 2017, the database holds data from almost 800 000 patients with prescriptions. All hospitals provide at least 18 mandatory data items per patient. Provided data include, among others, individual prescriptions, diagnosis data, and hospitalisation and survival data. The database is currently used to benchmark the level of biosimilar prescribing and to assess the impact of novel systemic treatments on survival rates in metastatic cancers.

Conclusion

We showed that it is feasible for a group of hospitals to construct their own database that can serve as a tool to benchmark the positioning of medicines and to start with monitoring their impact on clinical outcomes.

Privacy, Security, and Patient Engagement: The Changing Health Data Governance Landscape

CONTEXT

The rapid emergence of new technologies support collection and use of a wide variety of data from clinical, genomic, social and behavioral, environmental, and financial sources, and have a great impact on the governance of personal health information.

PAPERS IN THE SPECIAL ISSUE

The papers in this special issue on governance touch on the topic from a variety of focuses, including leadership perspectives, local and federal case studies, and the future importance of patient engagement.

THEMES

This special issue focuses on three major themes-that data governance is growing in importance and presenting new challenges that must be addressed, that health care organizations must prioritize governance design, implementation, and functions as a priority, and that governance seems to be naturally converging on an archetype as described by this set of papers.

FUTURE STATE OF GOVERNANCE

In order to deal with issues such as data de- and re-identification, data governance must be studied as its own field.

Research as a Standard of Care in the PICU

OBJECTIVES

Excellence in clinical care coupled with basic and applied research reflects the maturation of a medical subspecialty, advances that field, and provides objective data for identifying best practices. PICUs are uniquely suited for conducting translational and clinical research. In addition, multiple investigations have reported that a majority of parents are interested in their children's participation in clinical research, even when the research offers no direct benefit to their child. However, such activity may generate ethical conflict with bedside care providers trying to acutely identify the best approach for an individual critically ill child. Ultimately, this conflict may diminish enthusiasm for the generation of scientific evidence that supports the application of evidence-based medicine into PICU clinical standard work. Accordingly this review endeavors to provide an overview of current state PICU clinical research strengths, liabilities, opportunities, and barriers and contrast this with an established pediatric hematology-oncology iterative research model that constitutes a learning healthcare system.

DATA SOURCES, DATA EXTRACTION, AND DATA SYNTHESIS

Narrative review of medical literature published in English.

CONCLUSIONS

Currently, most PICU therapy is not evidence based. Developing a learning healthcare system in the PICU integrates clinical research into usual practice and fosters a culture of evidence-based learning and continual care improvement. As PICU mortality has significantly decreased, identification and validation of patient-centered, clinically relevant research outcome measures other than mortality is essential for future clinical trial design. Because most pediatric critical illness may be classified as rare diseases, participation in research networks will facilitate iterative, collaborative, multiinstitutional investigations that over time identify the best practices to improve PICU outcomes. Despite real ethical challenges, critically ill children and their families should have the opportunity to participate in translational/clinical research whenever feasible.

Evidence-based rules from family practice to inform family practice; the learning healthcare system case study on urinary tract infections

BACKGROUND

Analysis of encounter data relevant to the diagnostic process sourced from routine electronic medical record (EMR) databases represents a classic example of the concept of a learning healthcare system (LHS). By collecting International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all presenting reasons for encounter (RfEs) and recorded diagnostic outcomes. We have specifically looked at new episodes of care (EoC) for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis (ICPC code: U70).

METHODS

Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the ICPC, including RfEs presented by the patient, and the FDs' diagnostic labels. The relationships between RfEs and episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project.

RESULTS

The Dutch data indicated that the presence of RfE's "Cystitis/Urinary Tract Infection", "Dysuria", "Fear of UTI", "Urinary frequency/urgency", "Haematuria", "Urine symptom/complaint, other" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection" . The Maltese data indicated that the presence of RfE's "Dysuria", "Urinary frequency/urgency", "Haematuria" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection". The Dutch data indicated that the presence of RfE's "Flank/axilla symptom/complaint", "Dysuria", "Fever", "Cystitis/Urinary Tract Infection", "Abdominal pain/cramps general" are all strong, reliable, predictors for the diagnosis "Pyelonephritis" . The Maltese data set did not present any clinically and statistically significant predictors for pyelonephritis.

CONCLUSIONS

We describe clinically and statistically significant diagnostic associations observed between UTIs and pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate clinically meaningful diagnostic evidence from electronic sources of patient data.