What's Past is Prologue: A Scoping Review of Recent Public Health and Global Health Informatics Literature

Benefits, challenges, and contributors to success for national eHealth systems implementation: a scoping review

OBJECTIVE

Our scoping review aims to assess what legal, ethical, and socio-technical factors contribute to or inhibit the success of national eHealth system implementations. In addition, our review seeks to describe the characteristics and benefits of eHealth systems.

MATERIALS AND METHODS

We conducted a scoping review of literature published in English between January 2000 and 2020 using a keyword search on 5 databases: PubMed, Scopus, Web of Science, IEEEXplore, and ProQuest. After removal of duplicates, abstract screening, and full-text filtering, 86 articles were included from 8276 search results.

RESULTS

We identified 17 stakeholder groups, 6 eHealth Systems areas, and 15 types of legal regimes and standards. In-depth textual analysis revealed challenges mainly in implementation, followed by ethico-legal and data-related aspects. Key factors influencing success include promoting trust of the system, ensuring wider acceptance among users, reconciling the system with legal requirements, and ensuring an adaptable technical platform.

DISCUSSION

Results revealed support for decentralized implementations because they carry less implementation and engagement challenges than centralized ones. Simultaneously, due to decentralized systems' interoperability issues, federated implementations (with a set of national standards) might be preferable.

CONCLUSION

This study identifies the primary socio-technical, legal, and ethical factors that challenge and contribute to the success of eHealth system implementations. This study also describes the complexities and characteristics of existing eHealth implementation programs, and suggests guidance for resolving the identified challenges.

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.

Potential value of electronic health records in capturing data on geriatric frailty for population health

OBJECTIVES

Despite the availability of many frailty measures to identify older adults at risk, frailty instruments are not routinely used for risk assessment in population health management. Here, we assessed the potential value of electronic health records (EHRs) and administrative claims in providing the necessary data for variables used across various frailty instruments.

SETTING AND PARTICIPANTS

The review focused on studies conducted worldwide. Participants included older people aged 50 and older.

DESIGN

We identified frailty instruments published between 2011 and 2018. Frailty variables used in each of the frailty instruments were extracted, grouped, and categorized across health determinants and various clinical factors.

MEASURES

The availability of the extracted frailty variables across various data sources (e.g., EHRs, administrative claims, and surveys) was evaluated by experts.

RESULTS

We identified 135 frailty instruments, which contained 593 unique variables. Clinical determinants of health were the best represented variables across frailty instruments (n = 516; 87 %), unlike social and health services factors (n = 33; ∼5% and n = 32; ∼5%). Most frailty instruments require at least one variable that is not routinely available in EHRs or claims (n = 113; ∼83 %). Only 22 frailty instruments have the potential to completely rely on EHR (structured or free-text data) and/or claims data, and possibly be operationalized on a population-level.

CONCLUSIONS AND IMPLICATIONS

Frailty instruments continue to be highly survey-based. More research is therefore needed to develop EHR-based frailty instruments for population health management. This will permit organizations and societies to stratify risk and better allocate resources among different older adult populations.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims

BACKGROUND

Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs.

OBJECTIVES

To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims.

METHODS

We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex.

RESULTS

The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels.

CONCLUSIONS

Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts.

DISCLOSURES

No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

Improving Notifiable Disease Case Reporting Through Electronic Information Exchange-Facilitated Decision Support: A Controlled Before-and-After Trial

OBJECTIVE

Outbreak detection and disease control may be improved by simplified, semi-automated reporting of notifiable diseases to public health authorities. The objective of this study was to determine the effect of an electronic, prepopulated notifiable disease report form on case reporting rates by ambulatory care clinics to public health authorities.

METHODS

We conducted a 2-year (2012-2014) controlled before-and-after trial of a health information exchange (HIE) intervention in Indiana designed to prepopulate notifiable disease reporting forms to providers. We analyzed data collected from electronic prepopulated reports and "usual care" (paper, fax) reports submitted to a local health department for 7 conditions by using a difference-in-differences model. Primary outcomes were changes in reporting rates, completeness, and timeliness between intervention and control clinics.

RESULTS

Provider reporting rates for chlamydia and gonorrhea in intervention clinics increased significantly from 56.9% and 55.6%, respectively, during the baseline period (2012) to 66.4% and 58.3%, respectively, during the intervention period (2013-2014); they decreased from 28.8% and 27.5%, respectively, to 21.7% and 20.6%, respectively, in control clinics (P < .001). Completeness improved from baseline to intervention for 4 of 15 fields in reports from intervention clinics (P < .001), although mean completeness improved for 11 fields in both intervention and control clinics. Timeliness improved for both intervention and control clinics; however, reports from control clinics were timelier (mean, 7.9 days) than reports from intervention clinics (mean, 9.7 days).

CONCLUSIONS

Electronic, prepopulated case reporting forms integrated into providers' workflow, enabled by an HIE network, can be effective in increasing notifiable disease reporting rates and completeness of information. However, it was difficult to assess the effect of using the forms for diseases with low prevalence (eg, salmonellosis, histoplasmosis).

Explainable statistical learning in public health for policy development: the case of real-world suicide data

BACKGROUND

In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.

METHODS

Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide.

RESULTS

Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified.

CONCLUSIONS

We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations.

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.

Applications of information and communications technologies to public health: A scoping review using the MeSH term: "public health informatics"

OBJECTIVE

To investigate the public health domains, key informatics concepts, and information and communications technologies (ICTs) applied in articles that are tagged with the MeSH term "public health informatics" and primarily focus on applying ICTs to public health.

MATERIALS AND METHODS

The MeSH term "public health informatics" was searched on MEDLINE-PubMed. The results of the search were then screened in two steps in order to only include articles about applying ICTs to public health problems. First, articles were screened based on their titles and abstracts. Second, a full-text review was conducted to ensure the relevance of the included articles. All articles were charted based on public health domain, information technology, article type, and informatics concept.

RESULTS

515 articles were included. Communicable disease monitoring (N=235), public health policy and research (N=201), and public health awareness (N=85) constituted the majority of the articles. Inconsistent results were found regarding the validity of syndromic surveillance and the effectiveness of PHI integration within the healthcare systems.

DISCUSSION

PHI articles with an ICT focus cover a wide range of themes. Collectively, the included articles emphasized the need for further research in interoperability, data quality, appropriate data sources, accessible health information, and communication. The limitations of the study include:1) only one database was searched; 2) by using MeSH tags as a selection criterion, PHI articles without the "public health informatics" MeSH term were excluded.

CONCLUSION

Due to the multi-disciplinary nature of PHI, MeSH identifiers were not assigned consistently. Current MeSH-tagged articles indicate that a comprehensive approach is required to integrate PHI into the healthcare system.

Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department

Background

Most public health agencies expect reporting of diseases to be initiated by hospital, laboratory or clinic staff even though so-called passive approaches are known to be burdensome for reporters and produce incomplete as well as delayed reports, which can hinder assessment of disease and delay recognition of outbreaks. In this study, we analyze patterns of reporting as well as data completeness and timeliness for traditional, passive reporting of notifiable disease by two distinct sources of information: hospital and clinic staff versus clinical laboratory staff. Reports were submitted via fax machine as well as electronic health information exchange interfaces.

Methods

Data were extracted from all submitted notifiable disease reports for seven representative diseases. Reporting rates are the proportion of known cases having a corresponding case report from a provider, a faxed laboratory report or an electronic laboratory report. Reporting rates were stratified by disease and compared using McNemar’s test. For key data fields on the reports, completeness was calculated as the proportion of non-blank fields. Timeliness was measured as the difference between date of laboratory confirmed diagnosis and the date the report was received by the health department. Differences in completeness and timeliness by data source were evaluated using a generalized linear model with Pearson’s goodness of fit statistic.

Results

We assessed 13,269 reports representing 9034 unique cases. Reporting rates varied by disease with overall rates of 19.1% for providers and 84.4% for laboratories (p < 0.001). All but three of 15 data fields in provider reports were more often complete than those fields within laboratory reports (p <0.001). Laboratory reports, whether faxed or electronically sent, were received, on average, 2.2 days after diagnosis versus a week for provider reports (p <0.001).

Conclusions

Despite growth in the use of electronic methods to enhance notifiable disease reporting, there still exists much room for improvement.

Using Information Systems to Improve a Mid-Sized Local Health Department's Effectiveness in a Time of Rapid Change

BACKGROUND

Informatics capacity building is resource and personnel intensive. Many local health departments (LHDs) face tradeoffs between using their resources to carry out existing mandates and using resources to build their capacity, for example, through informatics, to deliver essential services in a more effective and efficient manner.

OBJECTIVE

The purpose of this case study is to describe how a mid-sized LHD built and used information systems to support its strategic objectives, clinical services, and surveillance.

METHODS

The mid-sized LHD described here was chosen for its "best practices" in informatics capacity building and use by NACCHO's study advisory committee. To conduct the case study, authors reviewed departmental documents and conducted semistructured interviews with key informants in the agency. Interviews were recorded, transcribed, thematically coded, and analyzed.

RESULTS AND CONCLUSIONS

Findings from the case study suggest that including capacity building in informatics as a strategic priority is one of the most effective ways to ensure that informatics are assessed, updated, and included in resource decisions. Leadership at all levels is critical to the successful implementation of informatics as is proactive partnership with community partners who have overlapping goals. The efficiency and effectiveness of LHDs rely on informatics capacity, especially when resources are challenged.

Local Health Departments' Partners and Challenges in Electronic Exchange of Health Information

BACKGROUND

Unprecedented amounts of data are produced by the health care and other sectors, presenting opportunities for local health departments (LHDs) to access these data. LHDs will need to participate in health information exchange (HIE) with a number of partners in order to benefit from these data resources. LHDs' participation in HIEs with specific partners has not been studied.

OBJECTIVES

To describe the level of and challenges in LHD participation in HIE with other partners, and variation by LHD population size and governance type.

DATA AND METHODS

This 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 with a 50% response rate. Survey data were cleaned, and bivariate comparisons were conducted using χ and Somer's D.

RESULTS

Substantial variation existed in LHDs' participation in HIE by type of exchange partner. Although 71% participated in HIE with the state departments of health, only 12% with jail/correctional health, 14% with health or county-based purchasing plans, and 15% with home health agencies. Compared with large LHDs (jurisdiction populations of ≥500 000), smaller LHDs were more likely to participate in HIE with state departments of health, but less likely with other exchange partners. The challenges to HIE participation were technological, and organizational/interorganizational in nature and variation existed by LHDs' population size and governance structure with respect to state authority.

CONCLUSIONS

Local public health agencies more commonly participate in HIE with some partners, but may need to improve HIE with many others. National strategies targeting an increase in HIE of LHDs may use our findings to focus those initiatives.

Clinical Data Systems to Support Public Health Practice: A National Survey of Software and Storage Systems Among Local Health Departments

CONTEXT

Numerous software and data storage systems are employed by local health departments (LHDs) to manage clinical and nonclinical data needs. Leveraging electronic systems may yield improvements in public health practice. However, information is lacking regarding current usage patterns among LHDs.

OBJECTIVE

To analyze clinical and nonclinical data storage and software types by LHDs.

DESIGN

Data came from the 2015 Informatics Capacity and Needs Assessment Survey, conducted by Georgia Southern University in collaboration with the National Association of County and City Health Officials.

PARTICIPANTS

A total of 324 LHDs from all 50 states completed the survey (response rate: 50%).

MAIN OUTCOME MEASURES

Outcome measures included LHD's primary clinical service data system, nonclinical data system(s) used, and plans to adopt electronic clinical data system (if not already in use). Predictors of interest included jurisdiction size and governance type, and other informatics capacities within the LHD. Bivariate analyses were performed using χ and t tests.

RESULTS

Up to 38.4% of LHDs reported using an electronic health record (EHR). Usage was common especially among LHDs that provide primary care and/or dental services. LHDs serving smaller populations and those with state-level governance were both less likely to use an EHR. Paper records were a common data storage approach for both clinical data (28.9%) and nonclinical data (59.4%). Among LHDs without an EHR, 84.7% reported implementation plans.

CONCLUSIONS

Our findings suggest that LHDs are increasingly using EHRs as a clinical data storage solution and that more LHDs are likely to adopt EHRs in the foreseeable future. Yet use of paper records remains common. Correlates of electronic system usage emerged across a range of factors. Program- or system-specific needs may be barriers or facilitators to EHR adoption. Policy makers can tailor resources to address barriers specific to LHD size, governance, service portfolio, existing informatics capabilities, and other pertinent characteristics.

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.