Electronic health information quality challenges and interventions to improve public health surveillance data and practice

Differences in lipid and blood pressure measurements between individuals with type 1 diabetes and the general population: a cross-sectional study

OBJECTIVES

Cardiovascular disease (CVD) is a precarious complication of type 1 diabetes (T1D). Alongside glycaemic control, lipid and blood pressure (BP) management are essential for the prevention of CVD. However, age-specific differences in lipid and BP between individuals with T1D and the general population are relatively unknown.

DESIGN

Cross-sectional study.

SETTING

Six diabetes outpatient clinics and individuals from the Lifelines cohort, a multigenerational cohort from the Northern Netherlands.

PARTICIPANTS

2178 adults with T1D and 146 22 individuals without diabetes from the general population.

PRIMARY AND SECONDARY OUTCOME MEASURES

Total cholesterol, low-density lipoprotein cholesterol (LDL-cholesterol), systolic BP (SBP) and diastolic BP (DBP), stratified by age group, glycated haemoglobin category, medication use and sex.

RESULTS

In total, 2178 individuals with T1D and 146 822 without diabetes were included in this study. Total cholesterol and LDL-cholesterol were lower and SBP and DBP were higher in individuals with T1D in comparison to the background population. When stratified by age and medication use, total cholesterol and LDL-cholesterol were lower and SBP and DBP were higher in the T1D population. Men with T1D achieved lower LDL-cholesterol levels both with and without medication in older age groups in comparison to women. Women with T1D had up to 8 mm Hg higher SBP compared with the background population, this difference was not present in men.

CONCLUSIONS

Lipid and BP measurements are not comparable between individuals with T1D and the general population and are particularly unfavourable for BP in the T1D group. There are potential sex differences in the management of LDL-cholesterol and BP.

Development and implementation of an interoperability tool across state public health agency's disease surveillance and immunization information systems

Public health information systems have historically been siloed with limited interoperability. The State of Minnesota's disease surveillance system (Minnesota Electronic Disease Surveillance System: MEDSS, ∼12 million total reportable events) and immunization information system (Minnesota Immunization Information Connection: MIIC, ∼130 million total immunizations) lacked interoperability between them and data exchange was fully manual. An interoperability tool based on national standards (HL7 and SOAP/web services) for query and response was developed for electronic vaccination data exchange from MIIC into MEDSS by soliciting stakeholder requirements (n = 39) and mapping MIIC vaccine codes (n = 294) to corresponding MEDSS product codes (n = 48). The tool was implemented in March 2022 and incorporates MIIC data into a new vaccination form in MEDSS with mapping of 30 data elements including MIIC demographics, vaccination history, and vaccine forecast. The tool was evaluated using mixed methods (quantitative analysis of user time, clicks, queries; qualitative review with users). Comparison of key tasks demonstrated efficiencies including vaccination data access (before: 50 clicks, >2 min; after: 4 clicks, 8 s) which translated directly to staff effort (before: 5 h/week; after: ∼17 min/week). This case study demonstrates the contribution of improving public health systems interoperability, ultimately with the goal of enhanced data-driven decision-making and public health surveillance.

Electronic health record data quality assessment and tools: a systematic review

OBJECTIVE

We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies.

MATERIALS AND METHODS

We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process.

RESULTS

We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology.

DISCUSSION

There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality.

CONCLUSION

Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.

Assessing the effect of electronic health information exchange on the completeness and validity of data for measuring viral load testing turnaround time in Nigeria

INTRODUCTION

Implementation of health information exchange has been shown to result in several benefits which includes the improvement in the completeness and timeliness of data for public health program monitoring and surveillance.

OBJECTIVE

The objective of this study was to assess the effect of implementing an electronic health information exchange (HIE) on the quality of data available to measure HIV viral load testing turnaround time (TAT) in Nigeria.

METHODS

We measured viral load data validity and completeness before the implementation of electronic health information exchange, and 6 months after implementation. Records of specimens collected at 30 healthcare facilities and tested in 3 Polymerase Chain Reaction (PCR) labs were analyzed. We define data completeness as the percentage of non-missing values and measured this value by specimens and by data elements in the dataset for calculating TAT. To examine data validity, we classified TAT segments with negative values and date fields that were not in International Organization for Standardization(ISO) standard date format as invalid. Validity was measured by specimens and by each TAT segment. Pearson's chi square was used to assess for improvements in validity and completeness post implementation of HIE.

RESULTS

15,226 records of specimens were analyzed at baseline and 18,022 records of specimens analyzed at endline. Data completeness for all specimens recorded increased significantly from 47% before HIE implementation to 67% six months after implementation (p < 0.01). Data validity also increased from 90% before implementation to 91% after implementation (p < 0.01) CONCLUSION: Our study demonstrated evidence of significant improvement in the quality of data available to measure viral load turnaround time with the implementation of HIE.

Electronic Case Reporting (eCR) of COVID-19 to Public Health: Implementation Perspectives from the Minnesota Department of Health

Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites) and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20,000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the six reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues and expanding to other reportable conditions.

Healthcare utilization and mental health outcomes among nonfatal shooting assault victims

Victims of nonfatal shooting (NFS) assaults suffer from emotional and physical trauma; however, little is understood about clinical care utilization patterns among victims. This study examines the healthcare utilization and mental health outcomes before and after an index NFS victimization. A longitudinal dataset of police and clinical data were linked at the individual level to define a cohort of NFS victims with one or more clinical encounter in the 24-months preceding an index NFS injury (N = 2,681) in Indianapolis, Indiana between 2005 and 2018. Mental health was defined using ICD diagnosis codes from any emergency department, inpatient, or outpatient encounter and clinical care utilization was the number of unique encounters within the 24-months preceding and following an index NFS injury. Multivariable logistic regression was conducted to examine factors associated with a mental health diagnosis in the post injury period. Analyses were conducted in October 2021-March 2022. Overall clinical care utilization (Mean: pre = 277.7 (SD 235.3) vs. post = 333.9 (SD 255.1), p < 0.001) and mental health prevalence (14.4% pre vs. 18.8% post, p < 0.001) increased in the 24-months following an index NFS compared to the prior 24-months. Preinjury mental health utilization increased the odds of receiving a mental health diagnosis in the 24-months following an index NFS injury - particularly for Black victims (Odds Ratio 1.69, 95% CI 1.01, 2.85). The findings indicate missed opportunities within the healthcare system to connect NFS victims with needed mental health services, as well as the importance of premorbid connection to mental health care.

Audit of the Sydney Local Health District Public Health Unit notification and contact tracing system during the first wave of COVID-19

OBJECTIVE

To conduct a real-time audit to assess a Continuous Quality Improvement (CQI) activity to improve the quality of public health data in the Sydney Local Health District (SLHD) Public Health Unit during the first wave of COVID-19.

METHODS

A real-time audit of the Notifiable Conditions Information Management System was conducted for positive cases of COVID-19 and their close contacts from SLHD. After recording missing and inaccurate data, the audit team then corrected the data. Multivariable regression models were used to look for associations with workload and time.

RESULTS

A total of 293 cases were audited. Variables measuring completeness were associated with improvement over time (p<0.0001), whereas those measuring accuracy reduced with increased workload (p=0.0003). In addition, the audit team achieved 100% data quality by correcting data.

CONCLUSION

Utilising a team, separate from operational staff, to conduct a real-time audit of data quality is an efficient and effective way of improving epidemiological data. Implications for public health: Implementation of CQI in a public health unit can improve data quality during times of stress. Auditing teams can also act as an intervention in their own right to achieve high-quality data at minimal cost. Together, this can result in timely and high-quality public health data.

Computer Informatics for Infection Control

Computer informatics have the potential to improve infection control outcomes in surveillance, prevention, and public health. Surveillance activities include surveillance of infections, device use, and facility/ward outbreak detection and investigation. Prevention activities include awareness of multidrug-resistant organism carriage on admission, identification of high-risk individuals or populations, reducing device use, and antimicrobial stewardship. Enhanced communication with public health and other health care facilities across networks includes automated electronic communicable disease reporting, syndromic surveillance, and regional outbreak detection. Computerized public health networks may represent the next major evolution in infection control. This article reviews the use of informatics for infection control.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI)

Introduction

As the health system seeks to leverage large-scale data to inform population outcomes, the informatics community is developing tools for analysing these data. To support data quality assessment within such a tool, we extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health.

Methods

We developed and tested methods to measure the completeness, timeliness and entropy of information. The new data quality methods were applied to over 100 million clinical messages received from emergency department information systems for use in public health syndromic surveillance systems.

Discussion

While completeness and entropy methods were implemented by the OHDSI community, timeliness was not adopted as its context did not fit with the existing OHDSI domains. The case report examines the process and reasons for acceptance and rejection of ideas proposed to an open-source community like OHDSI.

Public Health Informatics in Local and State Health Agencies: An Update From the Public Health Workforce Interests and Needs Survey

Objective:

To characterize public health informatics (PHI) specialists and identify the informatics needs of the public health workforce.

Design:

Cross-sectional study.

Setting:

US local and state health agencies.

Participants:

Employees from state health agencies central office (SHA-COs) and local health departments (LHDs) participating in the 2017 Public Health Workforce Interests and Needs Survey (PH WINS). We characterized and compared the job roles for self-reported PHI, “information technology specialist or information system manager” (IT/IS), “public health science” (PHS), and “clinical and laboratory” workers.

Main Outcome Measure:

Descriptive statistics for demographics, income, education, public health experience, program area, job satisfaction, and workplace environment, as well as data and informatics skills and needs.

Results:

A total of 17 136 SHA-CO and 26 533 LHD employees participated in the survey. PHI specialist was self-reported as a job role among 1.1% and 0.3% of SHA-CO and LHD employees. The PHI segment most closely resembled PHS employees but had less public health experience and had lower salaries. Overall, fewer than one-third of PHI specialists reported working in an informatics program area, often supporting epidemiology and surveillance, vital records, and communicable disease. Compared with PH WINS 2014, current PHI respondents' satisfaction with their job and workplace environment moved toward more neutral and negative responses, while the IT/IS, PHS, and clinical and laboratory subgroups shifted toward more positive responses. The PHI specialists were less likely than those in IT/IS, PHS, or clinical and laboratory roles to report gaps in needed data and informatics skills.

Conclusions:

The informatics specialists' role continues to be rare in public health agencies, and those filling that role tend to have less public health experience and be less well compensated than staff in other technically focused positions. Significant data and informatics skills gaps persist among the broader public health workforce.

Leveraging Informatics to Identify Reportable Cases: Pilot Findings on Electronic Case Reporting of Chlamydia and Gonorrhea

Consensus-based technical guidance for electronic case reporting (eCR) of sexually transmitted infections was implemented within existing health information technologies to automatically detect chlamydia and gonorrhea cases based on diagnosis and laboratory observation codes and build a case report using industry standards. The process was evaluated using 12 420 ambulatory encounters among adolescents and adults 15 years and older seen at 8 Chicago-area community health centers between May 1 and June 30, 2017. We tabulated the frequency of matches between the case detection logic and patient data and compared the eCR identified cases with paper case reports. This study found that eCR increased provider reporting when compared with paper reporting alone. While additional work across stakeholder groups is needed, these early findings suggest that broadly adopted eCR will decrease both provider and public health burden while improving reporting timeliness and data completion to support case investigation.

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).

Using standardised patients to assess the quality of medical records: an application and evidence from rural China

BACKGROUND

Medical records play a fundamental role in healthcare delivery, quality assessment and improvement. However, there is little objective evidence on the quality of medical records in low and middle-income countries.

OBJECTIVE

To provide an unbiased assessment of the quality of medical records for outpatient visits to rural facilities in China.

METHODS

A sample of 207 township health facilities across three provinces of China were enrolled. Unannounced standardised patients (SPs) presented to providers following standardised scripts. Three weeks later, investigators returned to collect medical records from each facility. Audio recordings of clinical interactions were then used to evaluate completeness and accuracy of available medical records.

RESULTS

Medical records were located for 210 out of 620 SP visits (33.8%). Of those located, more than 80% contained basic patient information and drug treatment when mentioned in visits, but only 57.6% recorded diagnoses. The most incompletely recorded category of information was patient symptoms (74.3% unrecorded), followed by non-drug treatments (65.2% unrecorded). Most of the recorded information was accurate, but accuracy fell below 80% for some items. The keeping of any medical records was positively correlated with the provider's income (β 0.05, 95% CI 0.01 to 0.09). Providers at hospitals with prescription review were less likely to record completely (β -0.87, 95% CI -1.68 to 0.06). Significant variation by disease type was also found in keeping of any medical record and completeness.

CONCLUSION

Despite the importance of medical records for health system functioning, many rural facilities have yet to implement systems for maintaining patient records, and records are often incomplete when they exist. Prescription review tied to performance evaluation should be implemented with caution as it may create disincentives for record keeping. Interventions to improve record keeping and management are needed.

Secondary Use of Laboratory Data: Potentialities and Limitations

Clinical databases have been developed in recent years especially during the course of all medical concerns including laboratory results. The information produced by the diagnostic laboratories have great impact on health care system with various secondary uses. These uses are sometimes as publishing new extracted information of laboratory reports which have been widely applied in the scientific journals. Nowadays, some large scale or national databases are also formed from the integration of these data from smaller centers in the field of human health in many countries. These databases are beneficial for different stakeholders who may need this information. Unfortunately, reviewing some of these uses has indicated lots of errors in quality control, test validity, uniformity and so on. More importantly, some of the diagnostic procedures have been applied in the clinical diagnostic laboratories without even preliminary clinical evaluation studies. Therefore, any taken conclusion from these analyzed data may not be reliable. This use requires checking the several specifications that have been notified in this study. Current review also intends to show how the correct information should be to extract for the scientific reports, or integrated in large scale databases.

Applying an Electronic Health Records Data Quality Framework Across Service Sectors: A Case Study of Juvenile Justice System Data

Context

Integrating electronic health records (EHR) with other sources of administrative data is key to identifying factors affecting the long-term health of traditionally underserved populations, such as individuals involved in the justice system. Linking existing administrative data from multiple sources overcomes many of the limitations of traditional prospective studies of population health, but the linking process assumes high levels of data quality and consistency within administrative data. Studies of EHR, unlike other types of administrative data, have provided guidance to evaluate the utility of big data for population health research.

Case Description

Here, an established EHR data quality framework was applied to identify and describe the potential shortcomings of administrative juvenile justice system data collected by one of four case management systems (CMSs) across 12 counties in a Midwest state. The CMS data were reviewed for logical inconsistencies and compared along the data quality dimensions of plausibility and completeness.

Major Themes

After applying the data quality framework, several patterns of logical inconsistencies within the data were identified. To resolve these inconsistencies, recommendations regarding data entry, review, and extraction are offered.

Conclusion

The recommendations related to achieving quality justice system data can be applied to future efforts to link administrative databases from multiple sources. Increasing trust in administrative data quality related to vulnerable populations ultimately improves knowledge of pressing public health concerns.

Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency

Background

Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.

Objectives

The study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.

Methods

The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.

Results

All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals.

Conclusion

With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

Defining Care Patterns and Outcomes Among Persons Living with HIV in Washington, DC: Linkage of Clinical Cohort and Surveillance Data

Background

Triangulation of data from multiple sources such as clinical cohort and surveillance data can help improve our ability to describe care patterns, service utilization, comorbidities, and ultimately measure and monitor clinical outcomes among persons living with HIV infection.

Objectives

The objective of this study was to determine whether linkage of clinical cohort data and routinely collected HIV surveillance data would enhance the completeness and accuracy of each database and improve the understanding of care patterns and clinical outcomes.

Methods

We linked data from the District of Columbia (DC) Cohort, a large HIV observational clinical cohort, with Washington, DC, Department of Health (DOH) surveillance data between January 2011 and June 2015. We determined percent concordance between select variables in the pre- and postlinked databases using kappa test statistics. We compared retention in care (RIC), viral suppression (VS), sexually transmitted diseases (STDs), and non-HIV comorbid conditions (eg, hypertension) and compared HIV clinic visit patterns determined using the prelinked database (DC Cohort) versus the postlinked database (DC Cohort + DOH) using chi-square testing. Additionally, we compared sociodemographic characteristics, RIC, and VS among participants receiving HIV care at ≥3 sites versus <3 sites using chi-square testing.

Results

Of the 6054 DC Cohort participants, 5521 (91.19%) were included in the postlinked database and enrolled at a single DC Cohort site. The majority of the participants was male, black, and had men who have sex with men (MSM) as their HIV risk factor. In the postlinked database, 619 STD diagnoses previously unknown to the DC Cohort were identified. Additionally, the proportion of participants with RIC was higher compared with the prelinked database (59.83%, 2678/4476 vs 64.95%, 2907/4476; P<.001) and the proportion with VS was lower (87.85%, 2277/2592 vs 85.15%, 2391/2808; P<.001). Almost a quarter of participants (23.06%, 1279/5521) were identified as receiving HIV care at ≥2 sites (postlinked database). The participants using ≥3 care sites were more likely to achieve RIC (80.7%, 234/290 vs 62.61%, 2197/3509) but less likely to achieve VS (72.3%, 154/213 vs 89.51%, 1869/2088). The participants using ≥3 care sites were more likely to have unstable housing (15.1%, 64/424 vs 8.96%, 380/4242), public insurance (86.1%, 365/424 vs 57.57%, 2442/4242), comorbid conditions (eg, hypertension) (37.7%, 160/424 vs 22.98%, 975/4242), and have acquired immunodeficiency syndrome (77.8%, 330/424 vs 61.20%, 2596/4242) (all P<.001).

Conclusions

Linking surveillance and clinical data resulted in the improved completeness of each database and a larger volume of available data to evaluate HIV outcomes, allowing for refinement of HIV care continuum estimates. The postlinked database also highlighted important differences between participants who sought HIV care at multiple clinical sites. Our findings suggest that combined datasets can enhance evaluation of HIV-related outcomes across an entire metropolitan area. Future research will evaluate how to best utilize this information to improve outcomes in addition to monitoring them.

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.

Hospital Adoption of Health Information Technology to Support Public Health Infrastructure

CONTEXT

Health information technology (IT) has the potential to improve the nation's public health infrastructure. In support of this belief, meaningful use incentives include criteria for hospitals to electronically report to immunization registries, as well as to public health agencies for reportable laboratory results and syndromic surveillance. Electronic reporting can facilitate faster and more appropriate public health response. However, it remains unclear the extent that hospitals have adopted IT for public health efforts.

OBJECTIVE

To examine hospital adoption of IT for public health and to compare hospitals capable of using and not using public health IT.

DESIGN

Cross-sectional design with data from the 2012 American Hospital Association annual survey matched with data from the 2013 American Hospital Association Information Technology Supplement. Multivariate logistic regression was used to compare hospital characteristics. Inverse probability weights were applied to adjust for selection bias because of survey nonresponse.

PARTICIPANTS

All acute care general hospitals in the United States that matched across the surveys and had complete data available were included in the analytic sample.

MAIN OUTCOME MEASURES

Three separate outcome measures were used: whether the hospital could electronically report to immunization registries, whether the hospital could send electronic laboratory results, and whether the hospital can participate in syndromic surveillance.

RESULTS

A total of 2841 hospitals met the inclusion criteria. Weighted results show that of these hospitals, 62.7% can electronically submit to immunization registries, 56.6% can electronically report laboratory results, and 54.4% can electronically report syndromic surveillance. Adjusted and weighted results from the multivariate analyses show that small, rural hospitals and hospitals without electronic health record systems lag in the adoption of public health IT capabilities.

CONCLUSION

While a majority of hospitals are using public health IT, the infrastructure still has significant room for growth. Differences in hospitals' adoption of public health IT may exacerbate existing health disparities.

Medication Use among Veterans across Health Care Systems

INTRODUCTION

Dual healthcare system use can create gaps and fragments of information for patient care. The Department of Veteran Affairs is implementing a health information exchange (HIE) program called the Virtual Lifetime Electronic Record (VLER), which allows providers to access and share information across healthcare systems. HIE has the potential to improve the safety of medication use. However, data regarding the pattern of outpatient medication use across systems of care is largely unknown. Therefore, the objective of this study is to describe the prevalence of medication dispensing across VA and non-VA health care systems among a cohort Veteran population.

METHODS

This study included all Veterans who had two outpatient visits or one inpatient visit at the Indianapolis VA during a 1-year period prior to VLER enrollment. Source of medication data was assessed at the subject level, and categorized as VA, INPC (non-VA), or both. The primary target was identification of sources for medication data. Then, we compared the mean number of prescriptions, as well as overall and pairwise differences in medication dispensing.

RESULTS

Out of 52,444 Veterans, 17.4% of subjects had medication data available in a regional HIE. On average, 40 prescriptions per year were prescribed for Veterans who used both sources compared to 29 prescriptions per year from VA only and 25 prescriptions per year from INPC only sources. The annualized prescription rate of Veterans in the dual use group was 36% higher than those who had only VA data available and 61% higher than those who had only INPC data available.

CONCLUSIONS

Our data demonstrated that 17.4% of subjects had medication use identified from non-VA sources, including prescriptions for antibiotics, antineoplastics, and anticoagulants. These data support the need for HIE programs to improve coordination of information, with the potential to reduce adverse medication interactions and improve medication safety.

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.

Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study

Objectives:

We examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework.

Background:

The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies.

Methods:

Two emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided.

Conclusions:

SPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012)1 was developed. The data set cleaned using Kahn’s framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.

Learning from a cluster randomized controlled trial to improve healthcare workers' access to prevention and care for tuberculosis and HIV in Free State, South Africa: the pivotal role of information systems

Background

Occupational tuberculosis (TB) continues to plague the healthcare workforce in South Africa. A 2-year cluster randomized controlled trial was therefore launched in 27 public hospitals in Free State province, to better understand how a combined workforce and workplace program can improve health of the healthcare workforce.

Objective

This mid-term evaluation aimed to analyze how well the intervention was being implemented, seek evidence of impact or harm, and draw lessons.

Methods

Both intervention and comparison sites had been instructed to conduct bi-annual and issue-based infection control assessments (when healthcare workers [HCW] are diagnosed with TB) and offer HCWs confidential TB and HIV counseling and testing, TB treatment and prophylaxis for HIV-positive HCWs. Intervention sites were additionally instructed to conduct quarterly workplace assessments, and also offer HCWs HIV treatment at their occupational health units (OHUs). Trends in HCW mortality, sick-time, and turnover rates (2005–2014) were analyzed from the personnel salary database (‘PERSAL’). Data submitted by the OHUs were also analyzed. Open-ended questionnaires were then distributed to OHU HCWs and in-depth interviews conducted at 17 of the sites to investigate challenges encountered.

Results

OHUs reported identifying and treating 23 new HCW cases of TB amongst the 1,372 workers who used the OHU for HIV and/or TB services; 39 new cases of HIV were also identified and 108 known-HIV-positive HCWs serviced. Although intervention-site workforces used these services significantly more than comparison-site healthcare staff (p<0.001), the data recorded were incomplete for both the intervention and comparison OHUs. An overall significant decline in mortality and turnover rates was documented over this period, but no significant differences between intervention and comparison sites; sick-time data proved unreliable. Severe OHU workload as well as residual confidentiality concerns prevented the proper implementation of protocols, especially workplace assessments and data recording. Particularly, the failure to implement computerized data collection required OHU staff to duplicate their operational data collection duties by also entering research paper forms. The study was therefore halted pending the implementation of a computerized system.

Conclusions

The significant differences in OHU use documented cannot be attributable to the intervention due to incomplete data reporting; unreliable sick-time data further precluded ascertaining the benefit potentially attributable to the intervention. Computerized data collection is essential to facilitate operational monitoring while conducting real-world intervention research. The digital divide still requires the attention of researchers along with overall infrastructural constraints.

Which veterans enroll in a VA health information exchange program?

OBJECTIVE

To characterize patients who voluntarily enrolled in an electronic health information exchange (HIE) program designed to share data between Veterans Health Administration (VHA) and non-VHA institutions.

MATERIALS AND METHODS

Patients who agreed to participate in the HIE program were compared to those who did not. Patient characteristics associated with HIE enrollment were examined using a multivariable logistic regression model. Variables selected for inclusion were guided by a health care utilization model adapted to explain HIE enrollment. Data about patients' sociodemographics (age, gender), comorbidity (Charlson index score), utilization (primary and specialty care visits), and access (distance to VHA medical center, insurance, VHA benefits) were obtained from VHA and HIE electronic health records.

RESULTS

Among 57 072 patients, 6627 (12%) enrolled in the HIE program during its first year. The likelihood of HIE enrollment increased among patients ages 50-64, of female gender, with higher comorbidity, and with increasing utilization. Living in a rural area and being unmarried were associated with decreased likelihood of enrollment.

DISCUSSION AND CONCLUSION

Enrollment in HIE is complex, with several factors involved in a patient's decision to enroll. To broaden HIE participation, populations less likely to enroll should be targeted with tailored recruitment and educational strategies. Moreover, inclusion of special populations, such as patients with higher comorbidity or high utilizers, may help refine the definition of success with respect to HIE implementation.

Shared Longitudinal Health Records for Clinical and Population Health

The ability of a health information exchange to consolidate information, collected in multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual’s medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter will describe the clinical benefits imparted by the shared health record (SHR) component of the OpenHIE infrastructure. It will also characterize the potential population health benefits of the aggregate level data contained and distributed by the Health Management Information System component of OpenHIE. The chapter will further discuss the implementation of these systems.

By the end of the chapter, the reader should be able to:●

Identify and describe the differences among an electronic medical record, electronic health record, and a shared heath record.

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Explain the role of a shared health record in a health information exchange.

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List and describe the components of a shared health record.

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Discuss the role and benefits of a health management information system within a health information exchange.

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Define a population health indicator.

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Identify and describe application domains for a health management information system.

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Define a database management system.

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Compare the implications of implementing a shared health record using an electronic health record system versus a database management system.

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Discuss emerging trends likely to shape the evolution of shared health records and health management information systems.

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

OBJECTIVE

To categorize and describe the public health informatics (PHI) and global health informatics (GHI) literature between 2012 and 2014.

METHODS

We conducted a semi-systematic review of articles published between January 2012 and September 2014 where information and communications technologies (ICT) was a primary subject of the study or a main component of the study methodology. Additional inclusion and exclusion criteria were used to filter PHI and GHI articles from the larger biomedical informatics domain. Articles were identified using MEDLINE as well as personal bibliographies from members of the American Medical Informatics Association PHI and GHI working groups.

RESULTS

A total of 85 PHI articles and 282 GHI articles were identified. While systems in PHI continue to support surveillance activities, we identified a shift towards support for prevention, environmental health, and public health care services. Furthermore, articles from the U.S. reveal a shift towards PHI applications at state and local levels. GHI articles focused on telemedicine, mHealth and eHealth applications. The development of adequate infrastructure to support ICT remains a challenge, although we identified a small but growing set of articles that measure the impact of ICT on clinical outcomes.

DISCUSSION

There is evidence of growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. Yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles.

CONCLUSION

While the body of knowledge around PHI and GHI continues to mature, additional studies of higher quality are needed to generate the robust evidence base needed to support continued investment in ICT by governmental health agencies.

The Challenges of Data Quality Evaluation in a Joint Data Warehouse

Introduction:

The use of clinically derived data from electronic health records (EHRs) and other electronic clinical systems can greatly facilitate clinical research as well as operational and quality initiatives. One approach for making these data available is to incorporate data from different sources into a joint data warehouse. When using such a data warehouse, it is important to understand the quality of the data. The primary objective of this study was to determine the completeness and concordance of common types of clinical data available in the Knowledge Program (KP) joint data warehouse, which contains feeds from several electronic systems including the EHR.

Methods:

A manual review was performed of specific data elements for 250 patients from an EHR, and these were compared with corresponding elements in the KP data warehouse. Completeness and concordance were calculated for five categories of data including demographics, vital signs, laboratory results, diagnoses, and medications.

Results:

In general, data elements for demographics, vital signs, diagnoses, and laboratory results were present in more cases in the source EHR compared to the KP. When data elements were available in both sources, there was a high concordance. In contrast, the KP data warehouse documented a higher prevalence of deaths and medications compared to the EHR.

Discussion:

Several factors contributed to the discrepancies between data in the KP and the EHR—including the start date and frequency of data feeds updates into the KP, inability to transfer data located in nonstructured formats (e.g., free text or scanned documents), as well as incomplete and missing data variables in the source EHR.

Conclusion:

When evaluating the quality of a data warehouse with multiple data sources, assessing completeness and concordance between data set and source data may be better than designating one to be a gold standard. This will allow the user to optimize the method and timing of data transfer in order to capture data with better accuracy.

Convergent evolution of health information management and health informatics: a perspective on the future of information professionals in health care

Clearly defined boundaries are disappearing among the activities, sources, and uses of health care data and information managed by health information management (HIM) and health informatics (HI) professionals. Definitions of the professional domains and scopes of practice for HIM and HI are converging with the proliferation of information and communication technologies in health care settings. Convergence is changing both the roles that HIM and HI professionals serve in their organizations as well as the competencies necessary for training future professionals. Many of these changes suggest a blurring of roles and responsibilities with increasingly overlapping curricula, job descriptions, and research agendas. Blurred lines in a highly competitive market create confusion for students and employers. In this essay, we provide some perspective on the changing landscape and suggest a course for the future. First we review the evolving definitions of HIM and HI. We next compare the current domains and competencies, review the characteristics as well as the education and credentialing of both disciplines, and examine areas of convergence. Given the current state, we suggest a path forward to strengthen the contributions HIM and HI professionals and educators make to the evolving health care environment.

The visit-data warehouse: enabling novel secondary use of health information exchange data

INTRODUCTION/OBJECTIVES

Health Information Exchange (HIE) efforts face challenges with data quality and performance, and this becomes especially problematic when data is leveraged for uses beyond primary clinical use. We describe a secondary data infrastructure focusing on patient-encounter, nonclinical data that was built on top of a functioning HIE platform to support novel secondary data uses and prevent potentially negative impacts these uses might have otherwise had on HIE system performance.

BACKGROUND

HIE efforts have generally formed for the primary clinical use of individual clinical providers searching for data on individual patients under their care, but many secondary uses have been proposed and are being piloted to support care management, quality improvement, and public health.

DESCRIPTION OF THE HIE AND BASE INFRASTRUCTURE

This infrastructure review describes a module built into the Healthix HIE. Healthix, based in the New York metropolitan region, comprises 107 participating organizations with 29,946 acute-care beds in 383 facilities, and includes more than 9.2 million unique patients. The primary infrastructure is based on the InterSystems proprietary Caché data model distributed across servers in multiple locations, and uses a master patient index to link individual patients' records across multiple sites. We built a parallel platform, the "visit data warehouse," of patient encounter data (demographics, date, time, and type of visit) using a relational database model to allow accessibility using standard database tools and flexibility for developing secondary data use cases. These four secondary use cases include the following: (1) tracking encounter-based metrics in a newly established geriatric emergency department (ED), (2) creating a dashboard to provide a visual display as well as a tabular output of near-real-time de-identified encounter data from the data warehouse, (3) tracking frequent ED users as part of a regional-approach to case management intervention, and (4) improving an existing quality improvement program that analyzes patients with return visits to EDs within 72 hours of discharge.

RESULTS/LESSONS LEARNED

Setting up a separate, near-real-time, encounters-based relational database to complement an HIE built on a hierarchical database is feasible, and may be necessary to support many secondary uses of HIE data. As of November 2014, the visit-data warehouse (VDW) built by Healthix is undergoing technical validation testing and updates on an hourly basis. We had to address data integrity issues with both nonstandard and missing HL7 messages because of varied HL7 implementation across the HIE. Also, given our HIEs federated structure, some sites expressed concerns regarding data centralization for the VDW. An established and stable HIE governance structure was critical in overcoming this initial reluctance.

CONCLUSIONS

As secondary use of HIE data becomes more prevalent, it may be increasingly necessary to build separate infrastructure to support secondary use without compromising performance. More research is needed to determine optimal ways of building such infrastructure and validating its use for secondary purposes.

Leveraging health information exchange to improve population health reporting processes: lessons in using a collaborative-participatory design process

Introduction:

Surveillance, or the systematic monitoring of disease within a population, is a cornerstone function of public health. Despite significant investment in information technologies (IT) to improve the public’s health, health care providers continue to rely on manual, spontaneous reporting processes that can result in incomplete and delayed surveillance activities.

Background:

Participatory design principles advocate including real users and stakeholders when designing an information system to ensure high ecological validity of the product, incorporate relevance and context into the design, reduce misconceptions designers can make due to insufficient domain expertise, and ultimately reduce barriers to adoption of the system. This paper focuses on the collaborative and informal participatory design process used to develop enhanced, IT-enabled reporting processes that leverage available electronic health records in a health information exchange to prepopulate notifiable-conditions report forms used by public health authorities.

Methods:

Over nine months, public health stakeholders, technical staff, and informatics researchers were engaged in a multiphase participatory design process that included public health stakeholder focus groups, investigator-engineering team meetings, public health survey and census regarding high-priority data elements, and codesign of exploratory prototypes and final form mock-ups.

Findings:

A number of state-mandated report fields that are not highly used or desirable for disease investigation were eliminated, which allowed engineers to repurpose form space for desired and high-priority data elements and improve the usability of the forms. Our participatory design process ensured that IT development was driven by end user expertise and needs, resulting in significant improvements to the layout and functionality of the reporting forms.

Discussion:

In addition to informing report form development, engaging with public health end users and stakeholders through the participatory design process provided new insights into public health workflow and allowed the team to quickly triage user requests while managing user expectations within the realm of engineering possibilities.

Conclusion:

Engaging public health, engineering staff, and investigators in a shared codesigning process ensured that the new forms will not only meet real-life needs but will also support development of a product that will be adopted and, ultimately, improve communicable and infectious disease reporting by clinicians to public health.

The long road to semantic interoperability in support of public health: experiences from two states

Proliferation of health information technologies creates opportunities to improve clinical and public health, including high quality, safer care and lower costs. To maximize such potential benefits, health information technologies must readily and reliably exchange information with other systems. However, evidence from public health surveillance programs in two states suggests that operational clinical information systems often fail to use available standards, a barrier to semantic interoperability. Furthermore, analysis of existing policies incentivizing semantic interoperability suggests they have limited impact and are fragmented. In this essay, we discuss three approaches for increasing semantic interoperability to support national goals for using health information technologies. A clear, comprehensive strategy requiring collaborative efforts by clinical and public health stakeholders is suggested as a guide for the long road towards better population health data and outcomes.

Zoonotic disease surveillance--inventory of systems integrating human and animal disease information

Although 65% of recent major disease outbreaks throughout the world have a zoonotic origin, there is still a sharp division among the disciplines into the human and animal health sectors. In the last few decades, a global integrative concept, often referred to as 'One Health', has been strongly endorsed. Surveillance and monitoring efforts are major components for effective disease prevention and control. As human health and animal health are inextricably linked, it is assumed that a cross-sectoral data interpretation of zoonotic disease information will improve their prevention, prediction and control. To provide an overview of existing systems throughout the world which integrate information from humans and animals on zoonotic diseases, a literature review was conducted. Twenty projects were identified and described regarding their concepts and realization. They all vary widely depending on their surveillance purpose, their structure and the source of information they use. What they have in common is that they quite often use data which have already been collected for another purpose. Therefore, the challenges of how to make use of such secondary data are of great interest.

Estimating increased electronic laboratory reporting volumes for meaningful use: implications for the public health workforce

Objective: To provide formulas for estimating notifiable disease reporting volume from ‘meaningful use’ electronic laboratory reporting (ELR). Methods: We analyzed two years of comprehensive ELR reporting data from 15 metropolitan hospitals and laboratories. Report volumes were divided by population counts to derive generalizable estimators. Results: Observed volume of notifiable disease reports in a metropolitan area were more than twice national averages. ELR volumes varied by institution type, bed count, and by the level of effort required of health department staff. Conclusions: Health departments may experience a significant increase in notifiable disease reporting following efforts to fulfill meaningful use requirements, resulting in increases in workload that may further strain public health resources. Volume estimators provide a method for predicting ELR transaction volumes, which may support administrative planning in health departments.

Creating value: unifying silos into public health business intelligence

Introduction:

Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible United States hospitals and 60 percent of eligible providers have used certified electronic health record (EHR) technology and received Meaningful Use incentive funds (HITECH Act1).

Technology:

Certified EHR technology could create new public health (PH) value through novel and rapidly evolving data-use opportunities, never before experienced by PH. The long-standing “silo” approach to funding has fragmented PH programs and departments,2 but the components for integrated business intelligence (i.e., tools and applications to help users make informed decisions) and maximally reuse data are available now.

Systems:

Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and services research (PHSSR) may identify gaps and solutions for the PH community to address.

Conclusion:

Technology and system approaches to leverage this information explosion to support a transformed health care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the learning health system.