Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel

Feasibility of establishing a multi-center research database using the electronic health record: The PURSUIT network

BACKGROUND

Although multi-center research is needed in pediatric urology, collaboration is impeded by differences in physician documentation and research resources. Electronic health record (EHR) tools offer a promising avenue to overcome these barriers.

OBJECTIVE

To assess the accuracy, completeness, and utilization of structured data elements across multiple practices.

STUDY DESIGN

A standardized template was developed and implemented at five academic pediatric urology practices to document clinic visits for patients with congenital hydronephrosis and/or vesicoureteral reflux. Data from standardized elements in the template and from pre-existing EHR fields were extracted into a secure database. A 20% random sample of infants with data from structured elements from 1/1/2020 and 4/30/2021 were identified and compared to manual chart review at sites with >100 charts; all other sites reviewed at least 20 charts. Manual chart review was standardized across sites and included: clinic and operative notes, orders linked to the clinic encounter, radiology results, and active medications. Accuracy of data extraction was evaluated by computing the kappa statistic and percentage agreement. For sites that had adopted the templates prior to 6/1/2019 (early adopters), a list of eligible patients with an initial clinic visit from 1/1/2020-7/27/2020 was generated using standardized reporting techniques and confirmed by manual chart review. Physician utilization of the template was then calculated by comparing patients with data obtained from the note template to the generated list of eligible patients.

RESULTS

230 patient records met study criteria. Agreement between manual chart review and data extracted from the EHR was high (>85%). Race, ethnicity and insurance data were misclassified in about 10-15% of cases; this was due to site-specific differences in how these fields were coded. Renal ultrasound was misclassified 12% of the time; this was primarily due to outside images documented in radiology results but not included in the clinical note. All other data elements had >90% agreement (Figure). Template utilization for early adopters was >75% (75.5-87.5%).

DISCUSSION

This is the first study in urology to demonstrate that use of structured data elements can support multi-center research. Limitations include: inclusion of only academic sites with the Epic EHR and lack of data on utilization and sustainability at sites without a prior history of structured template use.

CONCLUSIONS

Multi-center research collaboration using EHR-based data collection tools is feasible with generally high accuracy compared to manual chart review. Additionally, sites with a long history of template adoption have high levels of provider utilization.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Opioid analgesic use after ambulatory surgery: a descriptive prospective cohort study of factors associated with quantities prescribed and consumed

OBJECTIVES

To prospectively characterise: (1) postoperative opioid analgesic prescribing practices; (2) experience of patients undergoing elective ambulatory surgeries and (3) impact of patient risk for medication misuse on postoperative pain management.

DESIGN

Longitudinal survey of patients 7 days before and 7-14 days after surgery.

SETTING

Academic urban safety-net hospital.

PARTICIPANTS

181 participants recruited, 18 surgeons, follow-up data from 149 participants (82% retention); 54% women; mean age: 49 years.

INTERVENTIONS

None.

PRIMARY AND SECONDARY OUTCOME MEASURES

Total morphine equivalent dose (MED) prescribed and consumed, percentage of unused opioids.

RESULTS

Surgeons postoperatively prescribed a mean of 242 total MED per patient, equivalent to 32 oxycodone (5 mg) pills. Participants used a mean of 116 MEDs (48%), equivalent to 18 oxycodone (5 mg) pills (~145 mg of oxycodone remaining per patient). A 10-year increase in patient age was associated with 12 (95% CI (-2.05 to -0.35)) total MED fewer prescribed opioids. Each one-point increase in the preoperative Graded Chronic Pain Scale was associated with an 18 (6.84 to 29.60) total MED increase in opioid consumption, and 5% (-0.09% to -0.005%) fewer unused opioids. Prior opioid prescription was associated with a 55 (5.38 to -104.82) total MED increase in opioid consumption, and 19% (-0.35% to -0.02%) fewer unused opioids. High-risk drug use was associated with 9% (-0.19% to 0.002%) fewer unused opioids. Pain severity in previous 3 months, high-risk alcohol, use and prior opioid prescription were not associated with postoperative prescribing practices.

CONCLUSIONS

Participants with a preoperative history of chronic pain, prior opioid prescription, and high-risk drug use were more likely to consume higher amounts of opioid medications postoperatively. Additionally, surgeons did not incorporate key patient-level factors (eg, substance use, preoperative pain) into opioid prescribing practices. Opportunities to improve postoperative opioid prescribing include system changes among surgical specialties, and patient education and monitoring.

Identifying Common Predictors of Multiple Adverse Outcomes Among Elderly Adults With Type-2 Diabetes

OBJECTIVE

As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels.

RESEARCH DESIGN AND METHODS

Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set.

RESULTS

The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15-7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71-0.80).

CONCLUSIONS

Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.

Accuracy of the medication list in the electronic health record-implications for care, research, and improvement

Objective

Electronic medication lists may be useful in clinical decision support and research, but their accuracy is not well described. Our aim was to assess the completeness of the medication list compared to the clinical narrative in the electronic health record.

Methods

We reviewed charts of 30 patients with inflammatory bowel disease (IBD) from each of 6 gastroenterology centers. Centers compared IBD medications from the medication list to the clinical narrative.

Results

We reviewed 379 IBD medications among 180 patients. There was variation by center, from 90% patients with complete agreement between the medication list and clinical narrative to 50% agreement.

Conclusions

There was a range in the accuracy of the medication list compared to the clinical narrative. This information may be helpful for sites seeking to improve data quality and those seeking to use medication list data for research or clinical decision support.

Establishment of the MID-NET® medical information database network as a reliable and valuable database for drug safety assessments in Japan

Purpose

To establish a new medical information database network (designated MID‐NET^®) to provide real‐world data for drug safety assessments in Japan.

Methods

This network was designed and developed by the Ministry of Health, Labour and Welfare and the Pharmaceuticals and Medical Devices Agency in collaboration with 23 hospitals from 10 healthcare organizations across Japan. MID‐NET^® is a distributed and closed network system that connects all collaborative organizations through a central data center. A wide variety of data are available for analyses, including clinical and administrative information. Several coding standards are used to standardize the data stored in MID‐NET^® to allow the integration of information originating from different hospitals. A rigorous and consistent quality management system was implemented to ensure that MID‐NET^® data are of high quality and meet Japanese regulatory standards (good post‐marketing study practice and related guidelines).

Results

MID‐NET^® was successfully established as a reliable and valuable medical information database and was officially launched in April 2018. High data quality with almost 100% consistency was confirmed between original data in hospitals and the data stored in MID‐NET^®. A major advantage is that approximately 260 clinical laboratory test results are available for analysis.

Conclusions

MID‐NET^® is expected to be a major data source for drug safety assessments in Japan. Experiences and best practices established in MID‐NET^® may provide a model for the future development of similar database networks.

Choosing Among Common Data Models for Real-World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency.

Design and Refinement of a Data Quality Assessment Workflow for a Large Pediatric Research Network

Background:

Clinical data research networks (CDRNs) aggregate electronic health record data from multiple hospitals to enable large-scale research. A critical operation toward building a CDRN is conducting continual evaluations to optimize data quality. The key challenges include determining the assessment coverage on big datasets, handling data variability over time, and facilitating communication with data teams. This study presents the evolution of a systematic workflow for data quality assessment in CDRNs.

Implementation:

Using a specific CDRN as use case, the workflow was iteratively developed and packaged into a toolkit. The resultant toolkit comprises 685 data quality checks to identify any data quality issues, procedures to reconciliate with a history of known issues, and a contemporary GitHub-based reporting mechanism for organized tracking.

Results:

During the first two years of network development, the toolkit assisted in discovering over 800 data characteristics and resolving over 1400 programming errors. Longitudinal analysis indicated that the variability in time to resolution (15day mean, 24day IQR) is due to the underlying cause of the issue, perceived importance of the domain, and the complexity of assessment.

Conclusions:

In the absence of a formalized data quality framework, CDRNs continue to face challenges in data management and query fulfillment. The proposed data quality toolkit was empirically validated on a particular network, and is publicly available for other networks. While the toolkit is user-friendly and effective, the usage statistics indicated that the data quality process is very time-intensive and sufficient resources should be dedicated for investigating problems and optimizing data for research.

A Data Element-Function Conceptual Model for Data Quality Checks

Introduction:

In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives.

Methods:

The model defines a “data element”, the primary focus of the check, and a “function”, the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Permanente’s Center for Effectiveness and Safety Research (CESR).

Results:

The model was able to classify all checks. A total of 751 unique data elements and 24 unique functions were extracted. The top five frequent data element-function pairings for OHDSI were Person-Count (55 checks), Insurance-Distribution (17), Medication-Count (16), Condition-Count (14), and Observations-Count (13); for CESR, they were Medication-Variable Type (175), Medication-Missing (172), Medication-Existence (152), Medication-Count (127), and Socioeconomic Factors-Variable Type (114).

Conclusions:

This study shows the efficacy of the data element-function conceptual model for classifying DQ checks, demonstrates early promise of NLP-assisted knowledge acquisition, and reveals the great heterogeneity in the focus in DQ checks, confirming variation in intrinsic checks and use-case specific “fitness-for-use” checks.

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

CONTEXT.—

The laboratory total testing process includes preanalytic, analytic, and postanalytic phases, but most laboratory quality improvement efforts address the analytic phase. Expanding quality improvement to preanalytic and postanalytic phases via use of medical data warehouses, repositories that include clinical, utilization, and administrative data, can improve patient care by ensuring appropriate test utilization. Cross-department, multidisciplinary collaboration to address gaps and improve patient and system outcomes is beneficial.

OBJECTIVE.—

To demonstrate medical data warehouse utility for characterizing laboratory-associated quality gaps amenable to preanalytic or postanalytic interventions.

DESIGN.—

A multidisciplinary team identified quality gaps. Medical data warehouse data were queried to characterize gaps. Organizational leaders were interviewed about quality improvement priorities. A decision aid with elements including national guidelines, local and national importance, and measurable outcomes was completed for each gap.

RESULTS.—

Gaps identified included (1) test ordering; (2) diagnosis, detection, and documentation, and (3) high-risk medication monitoring. After examination of medical data warehouse data including enrollment, diagnoses, laboratory, pharmacy, and procedures for baseline performance, high-risk medication monitoring was selected, specifically alanine aminotransferase, aspartate aminotransferase, complete blood count, and creatinine testing among patients receiving disease-modifying antirheumatic drugs. The test utilization gap was in monitoring timeliness (eg, >60% of patients had a monitoring gap exceeding the guideline recommended frequency). Other contributors to selecting this gap were organizational enthusiasm, regulatory labeling, and feasibility of a significant laboratory role in addressing the gap.

CONCLUSIONS.—

A multidisciplinary process facilitated identification and selection of a laboratory medicine quality gap. Medical data warehouse data were instrumental in characterizing gaps.

LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results

Objective

Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse.

Methods

Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, "million") to numerals. Quality control included expert review.

Results

We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%).

Conclusion

Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

"Minimally invasive research?" Use of the electronic health record to facilitate research in pediatric urology

BACKGROUND

The electronic health record (EHR) was designed as a clinical and administrative tool to improve clinical patient care. Electronic healthcare systems have been successfully adopted across the world through use of government mandates and incentives.

METHODS

Using electronic health record, health information system, electronic medical record, health information systems, research, outcomes, pediatric, surgery, and urology as initial search terms, the literature focusing on clinical documentation data capture and the EHR as a potential resource for research related to clinical outcomes, quality improvement, and comparative effectiveness was reviewed. Relevant articles were supplemented by secondary review of article references as well as seminal articles in the field as identified by the senior author.

FINDINGS

US federal funding agencies, including the Agency for Healthcare Research and Quality, the Patient-Centered Outcomes Research Institute, the National Institutes of Health, and the Food and Drug Administration have recognized the EHR's role supporting research. The main approached to using EHR data include enhanced lists, direct data extraction, structured data entry, and unstructured data entry. The EHR's potential to facilitate research, overcoming cost and time burdens associated with traditional data collection, has not resulted in widespread use of EHR-based research tools.

CONCLUSION

There are strengths and weaknesses for all existing methodologies of using EHR data to support research. Collaboration is needed to identify the method that best suits the institution for incorporation of research-oriented data collection into routine pediatric urologic clinical practice.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®)

Introduction

Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.

Methods

PCORnet's Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.

Results

The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.

Discussion

The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

Missing laboratory results data in electronic health databases: implications for monitoring diabetes risk

Aim: Laboratory test (lab) results may be useful to detect incident diabetes in electronic health record and claims-based studies. Research design & methods: Using the Mini-Sentinel distributed database, we assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes. Results: Inclusion of lab results increased the number of diabetes outcomes identified by 21%. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome. Conclusion: Lab results can increase sensitivity of algorithms for detecting diabetes, and missing lab results are associated with much lower rates of diabetes ascertainment regardless of algorithm. Patterns of missing lab results may identify ascertainment bias.

The Role of Hemoglobin Laboratory Test Results for the Detection of Upper Gastrointestinal Bleeding Outcomes Resulting from the Use of Medications in Observational Studies

INTRODUCTION

The identification of upper gastrointestinal (UGI) bleeding and perforated ulcers in claims data typically relies on inpatient diagnoses. The use of hemoglobin laboratory results might increase the detection of UGI events that do not lead to hospitalization.

OBJECTIVES

Our objective was to evaluate whether hemoglobin results increase UGI outcome identification in electronic databases, using non-steroidal anti-inflammatory drugs (NSAIDs) as a test case.

METHODS

From three data partner sites within the Mini-Sentinel Distributed Database, we identified NSAID initiators aged ≥18 years between 2008 and 2013. Numbers of events and risks within 30 days after NSAID initiation were calculated for four mutually exclusive outcomes: (1) inpatient UGI diagnosis of bleeding or gastric ulcer (standard claims-based definition without laboratory results); (2) non-inpatient UGI diagnosis AND ≥3 g/dl hemoglobin decrease; (3) ≥3 g/dl hemoglobin decrease without UGI diagnosis in any clinical setting; (4) non-inpatient UGI diagnosis, without ≥3 g/dl hemoglobin decrease.

RESULTS

We identified 2,289,772 NSAID initiators across three sites. Overall, 45.3% had one or more hemoglobin result available within 365 days before or 30 days after NSAID initiation; only 6.8% had results before and after. Of 7637 potential outcomes identified, outcome 1 accounted for 21.7%, outcome 2 for 0.8%, outcome 3 for 34.3%, and outcome 4 for 43.3%. Potential cases identified by outcome 3 were largely not suggestive of UGI events. Outcomes 1, 2, and 4 had similar distributions of specific UGI diagnoses.

CONCLUSIONS

Using available hemoglobin result values combined with non-inpatient UGI diagnoses identified few additional UGI cases. Non-inpatient UGI diagnostic codes may increase outcome detection but would require validation.

Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected

PURPOSE

Our purpose was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data methods.

METHODS

Using the Mini-Sentinel Distributed Database from three sites, we selected three exposure-outcome scenarios with laboratory results as baseline confounders. We compared hazard ratios (HRs) or risk differences (RDs) and 95% confidence intervals (CIs) from models that omitted laboratory results, included only available results (complete cases), and included results after applying missing data methods (multiple imputation [MI] regression, MI predictive mean matching [PMM] indicator).

RESULTS

Scenario 1 considered glucose among second-generation antipsychotic users and diabetes. Across sites, glucose was available for 27.7-58.9%. Results differed between complete case and missing data models (e.g., olanzapine: HR 0.92 [CI 0.73, 1.12] vs 1.02 [0.90, 1.16]). Across-site models employing different MI approaches provided similar HR and CI; site-specific models provided differing estimates. Scenario 2 evaluated creatinine among individuals starting high versus low dose lisinopril and hyperkalemia. Creatinine availability: 44.5-79.0%. Results differed between complete case and missing data models (e.g., HR 0.84 [CI 0.77, 0.92] vs. 0.88 [0.83, 0.94]). HR and CI were identical across MI methods. Scenario 3 examined international normalized ratio (INR) among warfarin users starting interacting versus noninteracting antimicrobials and bleeding. INR availability: 20.0-92.9%. Results differed between ignoring INR versus including INR using missing data methods (e.g., RD 0.05 [CI -0.03, 0.13] vs 0.09 [0.00, 0.18]). Indicator and PMM methods gave similar estimates.

CONCLUSION

Multi-site studies must consider site variability in missing data. Different missing data methods performed similarly. Copyright © 2016 John Wiley & Sons, Ltd.

Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies

Introduction:

We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing.

Case Descriptions and Variation Among Sites:

We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).

Findings:

National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.

Conclusion:

Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.

Linkage of Laboratory Results to Medicare Fee-for-Service Claims

BACKGROUND

Medicare is the single largest purchaser of laboratory testing in the United States, yet test results associated with Medicare laboratory claims have historically not been available.

OBJECTIVE

The purpose of this study was to describe both the linkage of laboratory results data to Medicare claims and the completeness of these results data. In a subgroup of beneficiaries initiating angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, we also demonstrate the generalizability of Medicare beneficiaries with laboratory values compared with those without laboratory values. We end with a discussion of the limitations and potential uses of these linked data.

METHODS

We obtained information about laboratory orders and results for all Medicare fee-for-service beneficiaries in 10 eastern states who had outpatient laboratory tests conducted by a large national laboratory services vendor in 2011. Using a combination of direct identifiers and patient demographic characteristics, we linked patients in these laboratory data to Medicare beneficiaries, enabling us to associate test results with existing claims.

RESULTS

Nearly all patients in the laboratory data were able to be linked to Medicare beneficiaries. There were over 2 million distinct beneficiaries with nearly 125 million specific test results in the laboratory data. For specific tests ordered in an outpatient or office setting in these 10 states, between 5% and 15% of them had linked laboratory data. Medicare beneficiaries initiating angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers who had laboratory results data had similar patient characteristics to those without results data.

CONCLUSIONS

This novel linkage of laboratory results data to Medicare claims creates unprecedented opportunities for conducting comparative effectiveness research related to patient safety and quality.

Health care system-level factors associated with performance on Medicare STAR adherence metrics in a large, integrated delivery system

Background:

The Centers for Medicare and Medicaid Services provide significant incentives to health plans that score well on Medicare STAR metrics for cardiovascular disease risk factor medication adherence. Information on modifiable health system-level predictors of adherence can help clinicians and health plans develop strategies for improving Medicare STAR scores, and potentially improve cardiovascular disease outcomes.

Objective:

To examine the association of Medicare STAR adherence metrics with system-level factors.

Research Design:

A cross-sectional study.

Subjects:

A total of 129,040 diabetes patients aged 65 years and above in 2010 from 3 Kaiser Permanente regions.

Measures:

Adherence to antihypertensive, antihyperlipidemic, and oral antihyperglycemic medications in 2010, defined by Medicare STAR as the proportion of days covered ≥80%.

Results:

After controlling for individual-level factors, the strongest predictor of achieving STAR-defined medication adherence was a mean prescribed medication days’ supply of >90 days (RR=1.61 for antihypertensives, oral antihyperglycemics, and statins; all P<0.001). Using mail order pharmacy to fill medications >50% of the time was independently associated with better adherence with these medications (RR=1.07, 1.06, 1.07; P<0.001); mail order use had an increased positive association among black and Hispanic patients. Medication copayments ≤$10 for 30 days’ supply (RR=1.02, 1.02, 1.02; P<0.01) and annual individual out-of-pocket maximums ≤$2000 (RR=1.02, 1.01, 1.02; P<0.01) were also significantly associated with higher adherence for all 3 therapeutic groupings.

Conclusions:

Greater medication days’ supply and mail order pharmacy use, and lower copayments and out-of-pocket maximums, are associated with better Medicare STAR adherence. Initiatives to improve adherence should focus on modifiable health system-level barriers to obtaining evidence-based medications.

Adherence to guidelines for glucose assessment in starting second-generation antipsychotics

OBJECTIVES

In 2003, the US Food and Drug Administration issued warnings about hyperglycemia and diabetes with second-generation antipsychotics (SGAs); guidelines have recommended metabolic screening since 2004. However, little is known of contemporary practices of glucose screening among youth initiating SGAs. Our objective was to evaluate baseline glucose assessment among youth in the Mini-Sentinel Distributed Database starting an SGA.

METHODS

The cohort included youth ages 2 through 18 newly initiating SGAs January 1, 2006, through December 31, 2011, across 10 sites. Baseline glucose was defined as fasting/random glucose or hemoglobin A1c (GLU) measurement occurring relative to first SGA dispensing. Differences in GLU assessment were evaluated with χ(2) tests and logistic regression.

RESULTS

The cohort included 16,304 youth; 60% boys; mean age 12.8 years. Risperidone was most commonly started (43%). Eleven percent (n = 1858) had GLU assessed between 90 days before and 3 days after first dispensing. Assessment varied across SGAs (olanzapine highest), sites (integrated health care systems higher), ages (16-18 highest), years (2007 highest), and gender (female higher; all P < .001). GLU assessment among those starting olanzapine was more likely than among those starting quetiapine (odds ratio [OR]: 1.72 [95% confidence interval (CI): 1.37-2.18]), aripiprazole (OR: 1.49 [95% CI: 1.18-1.87]), or risperidone (OR: 1.61 [95% CI: 1.28-2.03]).

CONCLUSIONS

Few children and adolescents starting SGA have baseline glucose assessed. This is concerning because those at high diabetes risk may not be identified. Further, lack of screening impedes determining the contribution of SGAs to hyperglycemia development.

Informatics and the clinical laboratory

The nature of pathology services is changing under the combined pressures of increasing workloads, cost constraints and technological advancement. In the face of this, laboratory systems need to meet new demands for data exchange with clinical electronic record systems for test requesting and results reporting. As these needs develop, new challenges are emerging especially with respect to the format and content of the datasets which are being exchanged. If the potential for the inclusion of intelligent systems in both these areas is to be realised, the continued dialogue between clinicians and laboratory information specialists is of paramount importance. Requirements of information technology (IT) in pathology, now extend well beyond the provision of purely analytical data. With the aim of achieving seamless integration of laboratory data into the total clinical pathway, 'Informatics' - the art and science of turning data into useful information - is becoming increasingly important in laboratory medicine. Informatics is a powerful tool in pathology - whether in implementing processes for pathology modernisation, introducing new diagnostic modalities (e.g. proteomics, genomics), providing timely and evidence-based disease management, or enabling best use of limited and often costly resources. Providing appropriate information to empowered and interested patients - which requires critical assessment of the ever-increasing volume of information available - can also benefit greatly from appropriate use of informatics in enhancing self-management of long term conditions. The increasing demands placed on pathology information systems in the context of wider developmental change in healthcare delivery are explored in this review. General trends in medical informatics are reflected in current priorities for laboratory medicine, including the need for unified electronic records, computerised order entry, data security and recovery, and audit. We conclude that there is a need to rethink the architecture of pathology systems and in particular to address the changed environment in which electronic patient record systems are maturing rapidly. The opportunity for laboratory-based informaticians to work collaboratively with clinical systems developers to embed clinically intelligent decision support systems should not be missed.