Assessment of Personal Health Care Management and Chronic Disease Prevalence: Comparative Analysis of Demographic, Socioeconomic, and Health-Related Variables.

BACKGROUND: The use of personal health care management (PHM) is increasing rapidly within the United States because of implementation of health technology across the health care continuum and increased regulatory requirements for health care providers and organizations promoting the use of PHM, particularly the use of text messaging (short message service), Web-based scheduling, and Web-based requests for prescription renewals. Limited research has been conducted comparing PHM use across groups based on chronic conditions. OBJECTIVE: This study aimed to describe the overall utilization of PHM and compare individual characteristics associated with PHM in groups with no reported chronic conditions, with 1 chronic condition, and with 2 or more such conditions. METHODS: Datasets drawn from the National Health Interview Survey were analyzed using multiple logistic regression to determine the level of PHM use in relation to demographic, socioeconomic, or health-related factors. Data from 47,814 individuals were analyzed using logistic regression. RESULTS: Approximately 12.19% (5737/47,814) of respondents reported using PHM, but higher rates of use were reported by individuals with higher levels of education and income. The overall rate of PHM remained stable between 2009 and 2014, despite increased focus on the promotion of patient engagement initiatives. Demographic factors predictive of PHM use included people who were younger, non-Hispanic, and who lived in the western region of the United States. There were also differences in PHM use based on socioeconomic factors. Respondents with college-level education were over 2.5 times more likely to use PHM than respondents without college-level education. Health-related factors were also predictive of PHM use. Individuals with health insurance and a usual place for health care were more likely to use PHM than individuals with no health insurance and no usual place for health care. Individuals reporting a single chronic condition or multiple chronic conditions reported slightly higher levels of PHM use than individuals reporting no chronic conditions. Individuals with no chronic conditions who did not experience barriers to accessing health care were more likely to use PHM than individuals with 1 or more chronic conditions. CONCLUSIONS: The findings of this study illustrated the disparities in PHM use based on the number of chronic conditions and that multiple factors influence the use of PHM, including economics and education. These findings provide evidence of the challenge associated with engaging patients using electronic health information as the health care industry continues to evolve.

Discovering and identifying New York heart association classification from electronic health records.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes. METHODS: We accessed HF patients' data in a local EHR system and identified potential sources of NYHA, including local diagnosis codes, procedures, and clinical notes. We further investigated and compared the performances of rule-based versus machine learning-based natural language processing (NLP) methods to identify NYHA class from clinical notes. RESULTS: Of the 36,276 patients with a diagnosis of HF or a CRT implant, 19.2% had NYHA class mentioned at least once in their EHR. While NYHA class existed in descriptive fields association with diagnosis codes (31%) or procedure codes (2%), the richest source of NYHA class was clinical notes (95%). A total of 6174 clinical notes were matched with hospital-specific custom NYHA class diagnosis codes. Machine learning-based methods outperformed a rule-based method. The best machine-learning method was a random forest with n-gram features (F-measure: 93.78%). CONCLUSIONS: NYHA class is documented in different parts in EHR for HF patients and the documentation rate is lower than expected. NLP methods are a feasible way to extract NYHA class information from clinical notes.

A mHealth-based care model for improving hypertension control in stroke survivors: Pilot RCT.

PURPOSE: Hypertension (HTN) is significantly under-treated in stroke survivors. We examined usability and efficacy of a mHealth -based care model for improving post-stroke HTN control (Funding: AHRQ R21HS021794). METHODS: We used a RCT design. Planned study duration was 90 days. Intervention arm (IA) participants measured their BP daily using a smart phone and wireless BP monitor. This was transmitted automatically to the study database. Investigators (Physician + PharmD) made bi-weekly medication adjustments to achieve the BP goal. Control arm (CA) participants received a digital BP monitor and usual care. We examined Usability (measured with Marshfield System Usability Survey) and HTN control efficacy using an ITT (intent-to-treat) and as-treated (AT) analyses. RESULTS: Fifty participants (IA = 28; CA = 22) completed the study. The Marshfield survey question, "I thought the system was easy to use" mean score was 4.6, (5 = strongly agree). Mean SBP declined significantly between enrollment and study completion in the IA. In ITT, IA SBP declined 9.88 mm, p = 0.005. In AT, IA SBP declined 10.81 mm, p = 0.0036. CA SBP decline was 5-6 mm Hg (not significant). In the ITT, baseline HTN control (SBP < 140 mm Hg) was 50% in IA and CA. At study completion, HTN was controlled in 82% (23/28) of IA and 64% (14/22) of CA (p = 0.14). In the AT, HTN was controlled in 89% (23/26) of IA and 58% (14/24) of CA, (p = 0.015). CONCLUSION: A mHealth-based HTN care model had excellent usability and provided better HTN control than usual care in stroke survivors. CLINICAL TRIAL: gov: NCT01875094.

Multiple Factors Drive Opioid Prescribing at the Time of Discharge.

Multiple factors potentially influence pain intensity or frequency, and consequently the need for an opioid prescription. This study aims to identify factors associated with being discharged with an outpatient opioid prescription. We constructed a database containing clinical, non-clinical, and organizational variables from the EHR that are potentially relevant for ordering an opioid at discharge. Descriptive statistics of these variables and univariate association analysis reveal that all of the examined variables to be statistically significantly associated with opioid prescription at discharge. Further, we fitted a random forest model to examine the information content in the examined variables regarding whether a patient will be discharged with an opioid. The model resulted in a mean AUC of 0.84, suggesting the factors examined in this study in combination contain significant information regarding prescription of an opioid at discharge.

Quantifying the Effect of Data Quality on the Validity of an eMeasure.

Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178­urinary catheter removal after surgery). Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue. Results Catheter duration had the most impact on the CMS178 eMeasure with every 1% reduction in data quality causing a 1.21% increase in the number of missing events. For birth date and admission type, every 1% reduction in data quality resulted in a 1% increase in missing events. Conclusion This research demonstrated that the impact of data quality issues can be quantified using a generalized process and that the CMS178 eMeasure, as currently defined, may not measure how well an organization is meeting the intended best practice goal. Secondary use of EHR data is warranted only if the data are of sufficient quality. The assessment approach described in this study demonstrates how the impact of data quality issues on an eMeasure can be quantified and the approach can be generalized for other data analysis tasks. Healthcare organizations can prioritize data quality improvement efforts to focus on the areas that will have the most impact on validity and assess whether the values that are reported should be trusted.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

Automatic Methods to Extract New York Heart Association Classification from Clinical Notes.

Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) classification is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. However, NYHA is rarely stored in EHR structured data such information is often documented in unstructured clinical notes. In this study, we thus investigated the use of natural language processing (NLP) methods to identify NYHA classification from clinical notes. We collected 6,174 clinical notes that were matched with hospital-specific custom NYHA class diagnosis codes. Machine-learning based methods performed similar with a rule-based method. The best machine-learning method, support vector machine with n-gram features, performed the best (93% F-measure). Further validation of the findings is required.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model. In the process of successfully creating a hierarchical ontology for flowsheet data in i2b2, we uncovered a variety of informatics and technical issues described in this paper.

Application of An Ontology for Characterizing Data Quality For a Secondary Use of EHR Data.

OBJECTIVE: The goal of this study is to apply an ontology based assessment process to electronic health record (EHR) data and determine its usefulness in characterizing data quality for calculating an example eMeasure (CMS178). METHODS: The process uses a data quality ontology that references separate data quality, domain and task ontologies to compute measures based on proportions of constraints that are satisfied. These quantities indicate how well the data conforms to the domain and how well it fits the task. RESULTS: The process was performed on a de-identified 200,000 encounter sample from a hospital EHR. CodingConsistency was poor (44%) but DomainConsistency (97%) and TaskRelevance (95%) were very good. Improvements in the data quality Measures correlated with improvements in the eMeasure. CONCLUSION: This approach can encourage the development of new detailed Domain ontologies that can be reused for data quality purposes across different organizations' EHR data. Automating the data quality assessment process using this method can enable sharing of data quality metrics that may aid in making research results that use EHR data more transparent and reproducible.

The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials.

BACKGROUND: An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. METHODS: Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. RESULTS: Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). CONCLUSIONS: Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.

Lead Testing in a Pediatric Population: Underscreening and Problematic Repeated Tests.

CONTEXT: Underscreening and problematic repeat lead testing in children. OBJECTIVE: Identify proportion of underscreening for elevated blood-lead levels in children. For children who receive a lead test, measure the level of problematic repeat lead tests, defined as those with a high probability of not meeting recommended guidelines for lead testing in children measured using a combination of patients' age, test type and sequencing, days between tests, and encounter diagnosis coding. DESIGN: A population-based retrospective cross-sectional design. SETTING: All health care services organizations in the state of Minnesota that delivered health services to the defined study population. PARTICIPANTS: The study population was a Medicaid cohort of 12 436 children aged 0 to 18 years observed over a 1-year period. MAIN OUTCOME MEASURES: Proportion of eligible children not receiving at least 1 lead test; proportion of problematic repeat lead tests. RESULTS: Thirty-five percent of children who should have received at least 1 lead test (n = 1714) during the study period did not. A total of 1856 children had at least 1 lead test and 190 had 2 or more. Fifty percent (50%) of the repeat tests were identified as problematic, representing 5.1% of the lead tests performed. Repeat tests performed in different health systems than the systems where the initial tests were performed had 5.3 times greater odds (adjusted odds ratio: 5.3 [95% confidence interval, 2.8-9.9]) of being problematic. CONCLUSIONS: The current approach to delivering mandatory lead testing across the state Medicaid population does not ensure that children are appropriately tested and has potential inefficiencies in that testing when it does take place. Use of multiple health care systems is associated with increased potential inefficiencies. Future Medicaid accountable care agreements between the state Medicaid program and participating health systems should emphasize clear population accountability for test screenings to improve patients' safety. A central queryable health resource or health information exchange may enable this.

Do Pediatric Patients Who Receive Care Across Multiple Health Systems Have Higher Levels of Repeat Testing?

Repetition by clinicians of the same tests for a given patient is common. However, not all repeat tests are necessary for optimal care and can result in unnecessary hardship. Limited evidence suggests that an electronic health record may reduce redundant laboratory testing and imaging by making previous results accessible to physicians. The purpose of this study is to establish a baseline by characterizing repeat testing in a pediatric population and to identify significant risk factors associated with repeated tests, including the impact of using multiple health systems. A population-based retrospective cross-sectional design was used to examine initial and repeat test instances, defined as a second test following an initial test of the same type for the same patient. The study population consisted of 8760 children with 1-25 test claims over a 1-year period. The study setting included all health care service organizations in Minnesota that generated these claims. In all, 17.2% of tests met the definition of repeat test instances, with several risk factors associated with per patient repeat test levels. The incidence of repeat test instances per patient was significantly higher when patients received care from more than 1 health system (adjusted incidence rate ratio 1.4; 95% confidence interval: 1.3-1.5). Repeat test levels are significant in pediatric populations and potentially actionable. Interoperable health information technology may reduce the incidence of repeat test instances in pediatric populations by making prior test results readily accessible. (Population Health Management 2016;19:102-108).

Using Publicly Available Data to Characterize Consumers Use of Email to Communicate with Healthcare Providers.

The use of patient focused technology has been proclaimed as a means to improve patient satisfaction and improve care outcomes. The Center for Medicaid/Medicare Services, through its EHR Incentive Program, has required eligible hospitals and professionals to send and receive secure messages from patients in order to receive financial incentives and avoid reimbursement penalties. Secure messaging between providers and patients has the potential to improve communication and care outcomes. The purpose of this study was to use National Health Interview Series (NHIS) data to identify the patient characteristics associated with communicating with healthcare providers via email. Individual patient characteristics were analyzed to determine the likelihood of emailing healthcare providers. The use of email for this purpose is associated with educational attainment, having a usual place of receiving healthcare, income, and geography. Publicly available data such as the NHIS may be used to better understand trends in adoption and use of consumer health information technologies.

Modeling Flowsheet Data for Clinical Research.

Health care data included in clinical data repositories (CDRs) are increasingly used for quality reporting, business analytics and research; however, extended clinical data from interprofessional practice are seldom included. With the increasing emphasis on care coordination across settings, CDRs need to include data from all clinicians and be harmonized to understand the impact of their collaborative efforts on patient safety, effectiveness and efficiency. This study characterizes the extended clinical data derived from EHR flowsheet data that is available in the University of Minnesota's CDR and describes a process for creating an ontology that organizes that data so that it is more useful and accessible to researchers. The process is illustrated using a pressure ulcer ontology and compares ease of finding concepts in i2b2 for different data organization approaches. The challenges of the manual process and difficulties combining similar concepts are discussed.

Determinants of Consumer eHealth Information Seeking Behavior.

Patients are increasingly using the Internet and other technologies to engage in their own healthcare, but little research has focused on the determinants of consumer eHealth behaviors related to Internet use. This study uses data from 115,089 respondents to four years of the National Health Interview Series to identify the associations between one consumer eHealth behavior (information seeking) and demographics, health measures, and Personal Health Information Management (PHIM) (messaging, scheduling, refills, and chat). Individuals who use PHIM are 7.5 times more likely to search the internet for health related information. Just as health has social determinants, the results of this study indicate there are potential social determinants of consumer eHealth behaviors including personal demographics, health status, and healthcare access.

A Data Quality Ontology for the Secondary Use of EHR Data.

The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the important terms used to describe data quality concepts and harmonized into an ontology. Four high-level data quality dimensions ("correctness", "consistency", "completeness" and "currency") categorize 19 lower level measures. The ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases. A detailed example is presented to demonstrate its utility. The DQ ontology can make data validation more common and reproducible.

Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BACKGROUND: Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a "Learning Healthcare System" at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. METHODS: Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. RESULTS: The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. CONCLUSIONS: Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting.

Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIA's 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients.

The impact of electronic health records on people with diabetes in three different emergency departments.

OBJECTIVE: To evaluate if electronic health records (EHR) with prior clinical information have observable effects for patients with diabetes presenting to emergency departments (ED), we examined measures of quality and resource utilization. MATERIALS AND METHODS: Retrospective observational studies of patients in three ED (A=5510; B=4393; C=3324) were conducted comparing patients with prior information in the EHR to those without such information. Differences with respect to hospitalization, mortality, length of stay (LOS), and numbers of ED orders for tests, procedures and medications were examined after adjusting for age, gender, race, marital status, comorbidities and for acuity level within each ED. RESULTS: There were 7% fewer laboratory test orders at one ED and 3% fewer at another; fewer diagnostic procedures were performed at two of the sites. At one site 36% fewer medications were ordered. The odds of being hospitalized were lower for EHR patients at one site and hospital LOS was shorter at two of the sites. EHR patient ED LOS was 18% longer at one site. There was no demonstrable impact of an EHR on mortality. Results varied in magnitude and direction by site. DISCUSSION: The pattern of significant results varied by ED but tended to reveal reduced utilization and better outcomes for patients although EHR patients' ED LOS was longer at one site. CONCLUSIONS: The presence of prior information in an EHR may be a valuable adjunct in the care of diabetes patients in ED settings but the pattern of impact may vary from ED to ED.

Effects of time constraints on clinician-computer interaction: a study on information synthesis from EHR clinical notes.

BACKGROUND: Time is a measurable and critical resource that affects the quality of services provided in clinical practice. There is limited insight into the effects of time restrictions on clinicians' cognitive processes with the electronic health record (EHR) in providing ambulatory care. OBJECTIVE: To understand the impact of time constraints on clinicians' synthesis of text-based EHR clinical notes. METHODS: We used an established clinician cognitive framework based on a think-aloud protocol. We studied interns' thought processes as they accomplished a set of four preformed ambulatory care clinical scenarios with and without time restrictions in a controlled setting. RESULTS: Interns most often synthesized details relevant to patients' problems and treatment, regardless of whether or not the time available for task performance was restricted. In contrast to previous findings, subsequent information commonly synthesized by clinicians related most commonly to the chronology of clinical events for the unrestricted time observations and to investigative procedures for the time-restricted sessions. There was no significant difference in the mean number of omission errors and incorrect deductions when interns synthesized the EHR clinical notes with and without time restrictions (3.5±0.5 vs. 2.3±0.5, p=0.14). CONCLUSION: Our results suggest that the incidence of errors during clinicians' synthesis of EHR clinical notes is not increased with modest time restrictions, possibly due to effective adjustments of information processing strategies learned from the usual time-constrained nature of patient visits. Further research is required to investigate the effects of similar or more extreme time variations on cognitive processes employed with different levels of expertise, specialty, and with different care settings.