IT-enabled Community Health Interventions: Challenges, Opportunities, and Future Directions

Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study

Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges.

Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues.

Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively).

Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities.

Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa

BACKGROUND

Comorbidities are strong predictors of current and future healthcare needs and costs; however, comorbidities are not evenly distributed geographically. A growing need has emerged for comorbidity surveillance that can inform decision-making. Comorbidity-derived risk scores are increasingly being used as valuable measures of individual health to describe and explain disease burden in populations.

METHODS

This study assessed the geographical distribution of comorbidity and its associated financial implications among commercially insured individuals in South Africa (SA). A retrospective, cross-sectional analysis was performed comparing the geographical distribution of comorbidities for 2.6 million commercially insured individuals over 2016-2017, stratified by geographical districts in SA. We applied the Johns Hopkins ACG® System across the insurance claims data of a large health plan administrator in SA to measure comorbidity as a risk score for each individual. We aggregated individual risk scores to determine the average risk score per district, also known as the comorbidity index (CMI), to describe the overall disease burden of each district.

RESULTS

We observed consistently high CMI scores in districts of the Free State and KwaZulu-Natal provinces for all population groups before and after age adjustment. Some areas exhibited almost 30% higher healthcare utilization after age adjustment. Districts in the Northern Cape and Limpopo provinces had the lowest CMI scores with 40% lower than expected healthcare utilization in some areas after age adjustment.

CONCLUSIONS

Our results show underlying disparities in CMI at national, provincial, and district levels. Use of geo-level CMI scores, along with other social data affecting health outcomes, can enable public health departments to improve the management of disease burdens locally and nationally. Our results could also improve the identification of underserved individuals, hence bridging the gap between public health and population health management efforts.

A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers

OBJECTIVE

To provide a methodology for estimating the effect of U.S.-based Certified Electronic Health Records Technology (CEHRT) implemented by primary care physicians (PCPs) on a Healthcare Effectiveness Data and Information Set (HEDIS) measure for childhood immunization delivery.

MATERIALS AND METHODS

This study integrates multiple health care administrative data sources from 2010 through 2014, analyzed through an interrupted time series design and a hierarchical Bayesian model. We compared managed care physicians using CEHRT to propensity-score matched comparisons from network physicians who did not adopt CEHRT. Inclusion criteria for physicians using CEHRT included attesting to the Childhood Immunization Status clinical quality measure in addition to meeting "Meaningful Use" (MU) during calendar year 2013. We used a first-presence patient attribution approach to develop provider-specific immunization scores.

RESULTS

We evaluated 147 providers using CEHRT, with 147 propensity-score matched providers selected from a pool of 1253 PCPs practicing in Maryland. The estimate for change in odds of increasing immunization rates due to CEHRT was 1.2 (95% credible set, 0.88-1.73).

DISCUSSION

We created a method for estimating immunization quality scores using Bayesian modeling. Our approach required linking separate administrative data sets, constructing a propensity-score matched cohort, and using first-presence, claims-based childhood visit information for patient attribution. In the absence of integrated data sets and precise and accurate patient attribution, this is a reusable method for researchers and health system administrators to estimate the impact of health information technology on individual, provider-level, process-based, though outcomes-focused, quality measures.

CONCLUSION

This research has provided evidence for using Bayesian analysis of propensity-score matched provider populations to estimate the impact of CEHRT on outcomes-based quality measures such as childhood immunization delivery.

Health Education Serious Games Targeting Health Care Providers, Patients, and Public Health Users: Scoping Review

BACKGROUND

Serious educational games have shown effectiveness in improving various health outcomes. Previous reviews of health education games have focused on specific diseases, certain medical subjects, fixed target groups, or limited outcomes of interest. Given the recent surge in health game studies, a scoping review of health education games is needed to provide an updated overview of various aspects of such serious games.

OBJECTIVE

This study aimed to conduct a scoping review of the design and evaluation of serious educational games for health targeting health care providers, patients, and public (health) users.

METHODS

We identified 2313 studies using a unique combination of keywords in the PubMed and ScienceDirect databases. A total of 161 studies were included in this review after removing duplicates (n=55) and excluding studies not meeting our inclusion criteria (1917 based on title and abstract and 180 after reviewing the full text). The results were stratified based on games targeting health care providers, patients, and public users.

RESULTS

Most health education games were developed and evaluated in America (82/161, 50.9%) and Europe (64/161, 39.8%), with a considerable number of studies published after 2012. We discovered 58.4% (94/161) of studies aiming to improve knowledge learning and 41.6% (67/161) to enhance skill development. The studies targeted various categories of end users: health care providers (42/161, 26.1%), patients (38/161, 23.6%), public users (75/161, 46.6%), and a mix of users (6/161, 3.7%). Among games targeting patients, only 13% (6/44) targeted a specific disease, whereas a growing majority targeted lifestyle behaviors, social interactions, cognition, and generic health issues (eg, safety and nutrition). Among 101 studies reporting gameplay specifications, the most common gameplay duration was 30 to 45 min. Of the 61 studies reporting game repetition, only 14% (9/61) of the games allowed the users to play the game with unlimited repetitions. From 32 studies that measured follow-up duration after the game intervention, only 1 study reported a 2-year postintervention follow-up. More than 57.7% (93/161) of the games did not have a multidisciplinary team to design, develop, or assess the game.

CONCLUSIONS

Serious games are increasingly used for health education targeting a variety of end users. This study offers an updated scoping review of the studies assessing the value of serious games in improving health education. The results showed a promising trend in diversifying the application of health education games that go beyond a specific medical condition. However, our findings indicate the need for health education game development and adoption in developing countries and the need to focus on multidisciplinary teamwork in designing effective health education games. Furthermore, future health games should expand the duration and repetition of games and increase the length of the follow-up assessments to provide evidence on long-term effectiveness.

Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records

BACKGROUND

Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis.

METHODS

We included all people with diabetes (age at diagnosis 1.5-100 years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort.

RESULTS

There were 10,196 (T1D n = 60, T2D n = 10,136) and 5101 (T1D n = 43, T2D n = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms.

CONCLUSIONS

Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System

Background

Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs.

Objective

Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland.

Methods

We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR’s structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR’s unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR’s unstructured data.

Results

We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases–10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain.

Conclusions

Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach

Background

As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep.

Objective

This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.

Methods

Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.

Results

The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.

Conclusions

This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.

Comparing the Trends of Electronic Health Record Adoption Among Hospitals of the United States and Japan

The goal of this study is to examine the trends of Electronic Health Record (EHR) adoption among hospitals in Japan compared to those in the United States. Japan's nationwide survey of hospitals was utilized to extract the EHR adoption rates among Japanese hospitals. Comparable datasets from the Healthcare Information and Management System Society (HIMSS) and the American Hospital Association (AHA) were utilized to extract EHR adoption rates among U.S. hospitals. The trends of EHR adoption were stratified and analyzed by hospital size and hospital ownership status. As of 2014, the U.S. hospitals had a wider adoption of 'basic with clinical notes' EHRs compared to Japan (45.6% vs. 27.3%), but large hospitals (400+ beds) in Japan have shown a similar adoption rate of EHR systems than those of U.S. (65.6% vs. 68.5%). Governmental hospitals tend to be more advanced in EHR adoption than non-profit hospitals in Japan (53.0% vs. 21.5%). Non-profit hospitals show the highest adoption rate of 'basic' EHR systems in the U.S. as of 2014 (63.3%). Using the 'certified' definition of EHRs, the EHR adoption rate was close to 96% among U.S. hospitals as of 2016; however, updated EHR adoption data from Japanese hospitals has yet to be collected and published. U.S. and Japan have considerably increased EHR adoption among hospitals; however, this analysis indicates different trends of EHR adoption among hospitals by size and ownership status in both countries. Learnings from government programs supporting EHR adoption in the U.S. and Japan can be helpful in planning useful strategies for future hospital-oriented health IT policies in other developed nations.

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Background

Geriatric syndromes in older adults are associated with adverse outcomes. However, despite being reported in clinical notes, these syndromes are often poorly captured by diagnostic codes in the structured fields of electronic health records (EHRs) or administrative records.

Objective

We aim to automatically determine if a patient has any geriatric syndromes by mining the free text of associated EHR clinical notes. We assessed which statistical natural language processing (NLP) techniques are most effective.

Methods

We applied conditional random fields (CRFs), a widely used machine learning algorithm, to identify each of 10 geriatric syndrome constructs in a clinical note. We assessed three sets of features and attributes for CRF operations: a base set, enhanced token, and contextual features. We trained the CRF on 3901 manually annotated notes from 85 patients, tuned the CRF on a validation set of 50 patients, and evaluated it on 50 held-out test patients. These notes were from a group of US Medicare patients over 65 years of age enrolled in a Medicare Advantage Health Maintenance Organization and cared for by a large group practice in Massachusetts.

Results

A final feature set was formed through comprehensive feature ablation experiments. The final CRF model performed well at patient-level determination (macroaverage F1=0.834, microaverage F1=0.851); however, performance varied by construct. For example, at phrase-partial evaluation, the CRF model worked well on constructs such as absence of fecal control (F1=0.857) and vision impairment (F1=0.798) but poorly on malnutrition (F1=0.155), weight loss (F1=0.394), and severe urinary control issues (F1=0.532). Errors were primarily due to previously unobserved words (ie, out-of-vocabulary) and a lack of context.

Conclusions

This study shows that statistical NLP can be used to identify geriatric syndromes from EHR-extracted clinical notes. This creates new opportunities to identify patients with geriatric syndromes and study their health outcomes.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

BACKGROUND

The Meaningful Use (MU) program has promoted electronic health record adoption among US hospitals. Studies have shown that electronic health record adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of electronic health records (beyond MU) nor forecasted electronic health record maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals' electronic health record technology adoption and advancement patterns.

OBJECTIVE

This study forecasts the maturation of electronic health record functionality adoption among US hospitals through 2035.

METHODS

The Healthcare Information and Management Systems Society (HIMSS) Analytics' Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various electronic health record functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated electronic health record adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035.

RESULTS

In 2014, over 5400 hospitals completed HIMSS' annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as electronic health record adoption predates HIMSS' EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected "S-curve" for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035.

CONCLUSIONS

We forecasted the adoption of electronic health record capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in electronic health records as defined by EMRAM's Stage 7.

Distributed Data Networks That Support Public Health Information Needs

INTRODUCTION

Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems.

METHODS

We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability.

RESULTS

Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability.

CONCLUSION

Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland's All-Payer Global Budget

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland's unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH's EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland. First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.

Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims

BACKGROUND

There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification.

METHODS

This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined.

RESULTS

EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims.

DISCUSSION

The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

A proposed national research and development agenda for population health informatics: summary recommendations from a national expert workshop

OBJECTIVE

The Johns Hopkins Center for Population Health IT hosted a 1-day symposium sponsored by the National Library of Medicine to help develop a national research and development (R&D) agenda for the emerging field of population health informatics (PopHI).

MATERIAL AND METHODS

The symposium provided a venue for national experts to brainstorm, identify, discuss, and prioritize the top challenges and opportunities in the PopHI field, as well as R&D areas to address these.

RESULTS

This manuscript summarizes the findings of the PopHI symposium. The symposium participants' recommendations have been categorized into 13 overarching themes, including policy alignment, data governance, sustainability and incentives, and standards/interoperability.

DISCUSSION

The proposed consensus-based national agenda for PopHI consisted of 18 priority recommendations grouped into 4 broad goals: (1) Developing a standardized collaborative framework and infrastructure, (2) Advancing technical tools and methods, (3) Developing a scientific evidence and knowledge base, and (4) Developing an appropriate framework for policy, privacy, and sustainability. There was a substantial amount of agreement between all the participants on the challenges and opportunities for PopHI as well as on the actions that needed to be taken to address these.

CONCLUSION

PopHI is a rapidly growing field that has emerged to address the population dimension of the Triple Aim. The proposed PopHI R&D agenda is comprehensive and timely, but should be considered only a starting-point, given that ongoing developments in health policy, population health management, and informatics are very dynamic, suggesting that the agenda will require constant monitoring and updating.

Interoperability of Information Systems Managed and Used by the Local Health Departments

BACKGROUND

In the post-Affordable Care Act era marked by interorganizational collaborations and availability of large amounts of electronic data from other community partners, it is imperative to assess the interoperability of information systems used by the local health departments (LHDs).

OBJECTIVES

To describe the level of interoperability of LHD information systems and identify factors associated with lack of interoperability.

DATA AND METHODS

This mixed-methods research uses data from the 2015 Informatics Capacity and Needs Assessment Survey, with a target population of all LHDs in the United States. A representative sample of 650 LHDs was drawn using a stratified random sampling design. A total of 324 completed responses were received (50% response rate). Qualitative data were used from a key informant interview study of LHD informatics staff from across the United States. Qualitative data were independently coded by 2 researchers and analyzed thematically. Survey data were cleaned, bivariate comparisons were conducted, and a multivariable logistic regression was run to characterize factors associated with interoperability.

RESULTS

For 30% of LHDs, no systems were interoperable, and 38% of LHD respondents indicated some of the systems were interoperable. Significant determinants of interoperability included LHDs having leadership support (adjusted odds ratio [AOR] = 3.54), control of information technology budget allocation (AOR = 2.48), control of data systems (AOR = 2.31), having a strategic plan for information systems (AOR = 1.92), and existence of business process analysis and redesign (AOR = 1.49).

CONCLUSION

Interoperability of all systems may be an informatics goal, but only a small proportion of LHDs reported having interoperable systems, pointing to a substantial need among LHDs nationwide.