Enrichment sampling for a multi-site patient survey using electronic health records and census data

Objective

We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata.

Materials and Methods

This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations.

Results

The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys.

Discussion

We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate.

Conclusion

EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.

Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care

OBJECTIVE

To quantify compliance with guideline recommendations for secondary prevention in peripheral artery disease (PAD) using natural language processing (NLP) tools deployed to an electronic health record (EHR) and investigate provider opinions regarding clinical decision support (CDS) to promote improved implementation of these strategies.

PATIENTS AND METHODS

Natural language processing was used for automated identification of moderate to severe PAD cases from narrative clinical notes of an EHR of patients seen in consultation from May 13, 2015, to July 27, 2015. Guideline-recommended strategies assessed within 6 months of PAD diagnosis included therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and smoking abstention. Subsequently, a provider survey was used to assess provider knowledge regarding PAD clinical practice guidelines, comfort in recommending secondary prevention strategies, and potential role for CDS.

RESULTS

Among 73 moderate to severe PAD cases identified by NLP, only 12 (16%) were on 4 guideline-recommended strategies. A total of 207 of 760 (27%) providers responded to the survey; of these 141 (68%) were generalists and 66 (32%) were specialists. Although 183 providers (88%) managed patients with PAD, 51 (25%) indicated they were uncomfortable doing so; 138 providers (67%) favored the development of a CDS system tailored for their practice and 146 (71%) agreed that an automated EHR-derived mortality risk score calculator for patients with PAD would be helpful.

CONCLUSION

Natural language processing tools can identify cases from EHRs to support quality metric studies. Findings of this pilot study demonstrate gaps in application of guideline-recommended strategies for secondary risk prevention for patients with moderate to severe PAD. Providers strongly support the development of CDS systems tailored to assist them in providing evidence-based care to patients with PAD at the point of care.

Populating Physician Biographical Pages Based on EMR Data

The physicians' biographical pages are essential in providing information about physicians' specialties. However, physicians may not have biographical pages or the current pages are not comprehensive. We hypothesize that physicians' specialty information can be mined from Electronic Medical Records (EMRs) of their patients. We proposed an automated physician specialty populating (PSP) system that analyzes physician-ascertained diagnoses in EMRs, aggregates them to an appropriate granularity based on the current biographical pages, and populates the biographical pages accordingly. In this study, we applied the system using EMR data from Mayo Clinic and evaluated the system using the current biographical pages regarding various ranking strategies. Preliminary results demonstrated that using EMR data is a scalable and systematic way to populate physicians' biographical pages.

Linking medical and dental health record data: a partnership with the Rochester Epidemiology Project

PURPOSE

The purpose of this project was to expand the Rochester Epidemiology Project (REP) medical records linkage infrastructure to include data from oral healthcare providers. The goal of this linkage is to facilitate research studies examining the role of oral health in overall health and quality of life.

PARTICIPANTS

Eight dental practices joined the REP between 2011 and 2015. The REP study team has linked oral healthcare information with medical record information from local healthcare providers for 31 750 participants who have resided in Olmsted County, Minnesota. Overall, 17 718 (56%) participants are women, 14 318 (45%) are 40 years of age or older and 26 090 (82%) are white.

FINDINGS TO DATE

A first study using this new information was recently completed. This resource was used to determine whether the 2007 guidelines from the American Heart Association affected prescription rates of antibiotics to patients with moderate-risk cardiac conditions prior to dental procedures. The REP infrastructure was used to identify a series of patients diagnosed with moderate-risk cardiac conditions by the local healthcare providers (n=1351), and to abstract antibiotic prescriptions from dental records both pre-2007 and post-2007. Antibiotic prescriptions prior to dental procedures declined from 62% to 7% following the change in guidelines.

FUTURE PLANS

Dental data from participating practitioners will be updated on an annual basis, and new dental data will be linked to patient medical records. In addition, we will continue to invite new dental practices to participate in the REP. Finally, we will continue to use this research infrastructure to investigate associations between oral and medical health, and will present findings at conferences and in the scientific literature.

Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Why patients visit their doctors: assessing the most prevalent conditions in a defined American population

OBJECTIVE

To describe the prevalence of nonacute conditions among patients seeking health care in a defined US population, emphasizing age, sex, and ethnic differences.

PATIENTS AND METHODS

The Rochester Epidemiology Project (REP) medical records linkage system was used to identify all residents of Olmsted County, Minnesota, on April 1, 2009, who had consented to review of their medical records for research (142,377 patients). We then electronically extracted all International Classification of Diseases, Ninth Revision codes noted in the records of these patients by any health care institution between January 1, 2005, and December 31, 2009. We grouped International Classification of Diseases, Ninth Revision codes into clinical classification codes and then into 47 broader disease groups associated with health-related quality of life. Age- and sex-specific prevalence was estimated by dividing the number of individuals within each group by the corresponding age- and sex-specific population. Patients within a group who had multiple codes were counted only once.

RESULTS

We included a total of 142,377 patients, 75,512 (53%) of whom were female. Skin disorders (42.7%), osteoarthritis and joint disorders (33.6%), back problems (23.9%), disorders of lipid metabolism (22.4%), and upper respiratory tract disease (22.1%, excluding asthma) were the most prevalent disease groups in this population. Ten of the 15 most prevalent disease groups were more common in women in almost all age groups, whereas disorders of lipid metabolism, hypertension, and diabetes were more common in men. Additionally, the prevalence of 7 of the 10 most common groups increased with advancing age. Prevalence also varied across ethnic groups (whites, blacks, and Asians).

CONCLUSION

Our findings suggest areas for focused research that may lead to better health care delivery and improved population health.

Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system

The Rochester Epidemiology Project (REP) medical records-linkage system was established in 1966 to capture health care information for the entire population of Olmsted County, MN, USA. The REP includes a dynamic cohort of 502 820 unique individuals who resided in Olmsted County at some point between 1966 and 2010, and received health care for any reason at a health care provider within the system. The data available electronically (electronic REP indexes) include demographic characteristics, medical diagnostic codes, surgical procedure codes and death information (including causes of death). In addition, for each resident, the system keeps a complete list of all paper records, electronic records and scanned documents that are available in full text for in-depth review and abstraction. The REP serves as the research infrastructure for studies of virtually all diseases that come to medical attention, and has supported over 2000 peer-reviewed publications since 1966. The system covers residents of all ages and both sexes, regardless of socio-economic status, ethnicity or insurance status. For further information regarding the use of the REP for a specific study, please visit our website at www.rochesterproject.org or contact us at info@rochesterproject.org. Our website also provides access to an introductory video in English and Spanish.