Integration of Provider, Pharmacy, and Patient-Reported Data to Improve Medication Adherence for Type 2 Diabetes: A Controlled Before-After Pilot Study

Dixon, Brian E.; Alzeer, Abdullah; Phillips, Erin O’Kelly; Marrero, David G.

doi:10.2196/medinform.4739

Cited by 22 publications

(7 citation statements)

References 24 publications

(25 reference statements)

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“…This approach was identical to that taken by Moore et al 17 to define metabolic syndrome using NHANES data, except that the current study relies on patients’ medication use rather than physicians’ prescription to enhance accuracy because some struggle with medication adherence. 18 The continuous metabolic risk z score was used to supplement the dichotomous metabolic syndrome variable, because it incorporates components of metabolic disease risk into 1 variable. 15 To derive this score, continuously distributed measures of waist circumference, triglycerides (mg/dL), blood pressure (mm Hg systolic + mm Hg diastolic/2), 2-hour fasting glucose (mm/dL), and inverted fasting HDL-C (mg/dL) were standardized by subtracting the sample mean from the individual mean, and then dividing by the SD of the sample mean.…”

Section: Methodsmentioning

confidence: 99%

Long-Term Weight Loss and Metabolic Health in Adults Concerned With Maintaining or Losing Weight: Findings From NHANES

Knell

Gabriel

et al. 2018

Mayo Clinic Proceedings

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More than two-thirds of American adults are overweight or obese, with many attempting to lose weight to avoid adverse health outcomes and improve well-being. Achieving long-term weight loss (LTWL) success, defined as reaching at least a 5% to 10% weight loss goal, is challenging, yet important for overall metabolic health. It is currently unclear whether achieving higher thresholds of LTWL is associated with improved health. Therefore, the purpose of this study was to examine the association between LTWL thresholds (5%-9.9%, 10%-14.9%, 15%-19.9%, ≥20%) and metabolic health (metabolic syndrome and metabolic risk z score) among 7670 US adult respondents to the National Health and Nutrition Examination Survey (2007-2014) who were overweight or obese (past or present), were not underweight in the past year, not pregnant, and attempting to lose or maintain weight. A subsample of 3362 participants was used in the analysis of the metabolic risk z score. Multivariable regression models were constructed adjusting for covariates. Results indicate that the lowest and the 2 highest LTWL thresholds were related to lower odds for metabolic syndrome; for example, greater than or equal to 20% LTWL (odds ratio=0.52; 95% CI, 0.23-0.44; P<.001). All LTWL thresholds were significantly associated with the metabolic risk z score, with the largest effect among the 2 highest LTWL thresholds, that is, 15% to 19.9% LTWL (β=-0.45; 95% CI, -0.54 to -0.36; P<.001) and greater than or equal to 20% LTWL (β=-0.35; 95% CI, -0.53 to -0.17; P<.001). In conclusion, although achieving the currently recommended LTWL target was related to improved metabolic health, the 15% LTWL threshold was associated with more favorable outcomes.

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Section: Methodsmentioning

confidence: 99%

Long-Term Weight Loss and Metabolic Health in Adults Concerned With Maintaining or Losing Weight: Findings From NHANES

Knell

Gabriel

et al. 2018

Mayo Clinic Proceedings

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“…Achieving real-world practice change takes a long time and is often incomplete [35-37]. EHR-based clinical decision support has been shown to improve clinical process measures across multiple clinical domains [38-48].…”

Section: Discussionmentioning

confidence: 99%

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

et al. 2019

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BackgroundDefining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition.ObjectiveThe goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness.MethodsStarting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians.ResultsThe 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms.ConclusionsIn the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to i...

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“…Once our RHTN computable phenotype is validated, we can subsequently explore the effect of the different drug classification methods on the RHTN phenotype in future work. Other future work will include incorporating RxNorm Term Type (SCDF, SCD, SBD, etc), indicating retired RxCUIs, expanding to other disease states (diabetes, chronic kidney disease, heart failure, etc), and exploring the application to clinical decision support within the EHR [34,35].…”

Section: Xsl • Fomentioning

confidence: 99%

Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

McDonough¹,

Smith²,

Cooper‐DeHoff³

et al. 2020

JMIR Med Inform

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Background Computable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. Objective This study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes. Methods We compared 4 different antihypertensive drug classification systems based off of 4 different methodologies and terminologies, including 3 RxNorm Concept Unique Identifier (RxCUI)–based classifications and 1 medication name–based classification. The RxCUI-based classifications utilized data from (1) the Drug Ontology, (2) the new Medication Reference Terminology, and (3) the Anatomical Therapeutic Chemical Classification System and DrugBank, whereas the medication name–based classification relied on antihypertensive drug names. Each classification system was applied to EHR-based prescription drug data from hypertensive patients in the OneFlorida Data Trust. Results There were 13,627 unique RxCUIs and 8025 unique medication names from the 13,879,046 prescriptions. We observed a broad overlap between the 4 methods, with 84.1% (691/822) to 95.3% (695/729) of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms, drug products with an indication of benign prostatic hyperplasia, drug products that contain more than 1 ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs. Conclusions In total, 2 antihypertensive drug classifications were constructed, one based on RxCUIs and one based on medication name, that can be used in future computable phenotypes that require antihypertensive drug classifications.

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Integration of Provider, Pharmacy, and Patient-Reported Data to Improve Medication Adherence for Type 2 Diabetes: A Controlled Before-After Pilot Study

Cited by 22 publications

References 24 publications

Long-Term Weight Loss and Metabolic Health in Adults Concerned With Maintaining or Losing Weight: Findings From NHANES

Long-Term Weight Loss and Metabolic Health in Adults Concerned With Maintaining or Losing Weight: Findings From NHANES

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

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