Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Evaluation of weight change and cardiometabolic risk factors in a real-world population of US adults with overweight or obesity.

Whether individuals in real-world settings are able to lose weight and improve cardiometabolic risk factors over time is unclear. We aimed to determine the management of and degree of body weight change over 2 years among individuals with overweight or obesity, and to assess associated changes in cardiometabolic risk factors and clinical outcomes. Using data from 11 large health systems within the Patient-Centered Outcomes Research Network in the U.S., we collected the following data on adults with a recorded BMI ≥25 kg/m2 between January 1, 2016 and December 31, 2016: body-mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDLC), triglycerides and glycated hemoglobin (HbA1c). We found that among 882,712 individuals with BMI ≥25 kg/m2 (median age 59 years; 56% female), 52% maintained stable weight over 2 years and 1.3% utilized weight loss pharmacotherapy. Weight loss of 10% was associated with small but significant lowering of mean SBP (-2.69 mmHg [95% CI -2.88, -2.50]), DBP (-1.26 mmHg [95% CI -1.35, -1.18]), LDL-C (-2.60 mg/dL [95% CI -3.14, -2.05]), and HbA1c (-0.27% [95% CI -0.35, -0.19]) in the same 12 months. However, these changes were not sustained over the following year. In this study of adults with BMI ≥25 kg/m2, the majority had stable weight over 2 years, pharmacotherapies for weight loss were under-used, and small changes in cardiometabolic risk factors with weight loss were not sustained, possibly due to failure to maintain weight loss.

Implementing a home-based virtual hypertension programme-a pilot feasibility study.

INTRODUCTION: Implementing a health system-based hypertension programme may lower blood pressure (BP). METHODS: We performed a randomized, controlled pilot study to assess feasibility, acceptability, and safety of a home-based virtual hypertension programme integrating evidence-based strategies to overcome current barriers to BP control. Trained clinical pharmacists staffed the virtual collaborative care clinic (vCCC) to remotely manage hypertension using a BP dashboard and phone "visits" to monitor BP, adherence, side effects of medications, and prescribe anti-hypertensives. Patients with uncontrolled hypertension were identified via electronic health records. Enrolled patients were randomized to either vCCC or usual care for 3 months. We assessed patients' home BP monitoring behaviour, and patients', physicians', and pharmacists' perspectives on feasibility and acceptability of individual programme components. RESULTS: Thirty-one patients (vCCC = 17, usual care = 14) from six physician clinics completed the pilot study. After 3 months, average BP decreased in the vCCC arm (P = 0.01), but not in the control arm (P = 0.45). The vCCC participants measured BP more (9.9 vs. 1.2 per week, P < 0.001). There were no intervention-related adverse events. Participating physicians (n = 6), pharmacists (n = 5), and patients (n = 31) rated all programme components with average scores of >4.0, a pre-specified benchmark. Nine adaptations in vCCC design and delivery were made based on potential barriers to implementing the programme and suggestions. CONCLUSION: A home-based virtual hypertension programme using team-based care, technology, and a logical integration of evidence-based strategies is safe, acceptable, and feasible to intended users. These pilot data support studies to assess the effectiveness of this programme at a larger scale.

A secondary analysis of PAIN-CONTRoLS: Pain's impact on sleep, fatigue, and activities of daily living.

INTRODUCTION/AIMS: Peripheral neuropathies commonly affect quality of life of patients due to pain, sleep disturbances, and fatigue, although trials have not adequately explored these domains of care. The aim of this study was to assess the impact of nortriptyline, duloxetine, pregabalin, and mexiletine on pain, sleep, and fatigue in patients diagnosed with cryptogenic sensory polyneuropathy (CSPN). METHODS: We implemented a Bayesian adaptive design to perform a 12-wk multisite, randomized, prospective, open-label comparative effectiveness study in 402 CSPN patients. Participants received either nortriptyline (n = 134), duloxetine (n = 126), pregabalin (n = 73), or mexiletine (n = 69). At prespecified analysis timepoints, secondary outcomes, Patient Reported Outcomes Measurement Information System (PROMIS) surveys including Short Form (SF)-12, pain interference, fatigue, and sleep disturbance, were collected. RESULTS: Mexiletine had the highest quit rate (58%) due to gastrointestinal side effects, while nortriptyline (38%) and duloxetine (38%) had the lowest quit rates. If tolerated for the full 12 wk of the study, mexiletine had the highest probability (>90%) of positive outcomes for improvements in pain interference and fatigue. There was no significant difference among the medications for sleep disturbance or SF-12 scores. Adverse events and lack of efficacy were the two most common reasons for cessation of therapy. DISCUSSION: Physicians caring for patients with CSPN should consider mexiletine to address pain and fatigue, although nortriptyline and duloxetine are better medications to trial first since they are better tolerated. Future research should compare other commonly used medications for CSPN to determine evidence-based treatment strategies.

Type 2 Diabetes Affects Joint Pain Severity in People with Localized Osteoarthritis: A Retrospective Study.

OBJECTIVE: To examine the association between type 2 diabetes (T2D) and pain severity in people with localized osteoarthritis (OA) and to explore the association between glycemic control, measured by hemoglobin A1c (HbA1c) level, and pain severity in people with localized OA and T2D. DESIGN: Retrospective study. SETTING: A tertiary medical center. SUBJECTS: Data from 819 patients (mean age = 65.08±9.77 years, 54.3% women) were used. METHODS: Patients were grouped to localized OA only (N = 671) and localized OA+T2D (N = 148) based on diagnosis codes. An index date was set as the first diagnosis date of localized OA and linked to pain severity, measured by numeric rating scale from 0 to 10. HbA1c values were obtained for patients with T2D within six months of the index date. Multiple linear regression was used. RESULTS: After controlling for age, gender, body mass index (BMI); diagnoses of depression, hypertension, dyslipidemia; OA locations; and medication list (+/- 90 days of the index date), T2D was significantly associated with increased pain severity (B = 1.07, 95% confidence interval [CI] = 0.25 to 1.88, P = 0.014). For patients with T2D and localized OA with available data for HbA1c (N = 87), the results showed that an increased HbA1c value was significantly associated with higher pain severity (B = 0.36, 95% CI = 0.036 to 0.67, P = 0.029) after controlling for age, gender, BMI, medications, and OA locations. CONCLUSION: T2D was associated with higher pain severity in people with localized OA, and poor glycemic control was associated with higher pain severity in people with localized OA+T2D. Clinicians should emphasize that better HbA1c control might help with pain management in people with T2D and OA.

Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.

BACKGROUND: Likelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR). METHODS: We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models. RESULTS: In our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18-35 (7.29%), 36-55 (8.82%), 56-65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18-35, 36-55, 56-65, and > 65 were 0.784 (95% CI, 0.769-0.800), 0.766 (95% CI, 0.754-0.777), 0.754 (95% CI, 0.741-0.768), and 0.725 (95% CI, 0.709-0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups. CONCLUSIONS: As complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.

Association between Opioid Dose, Acute Post-operative Pain and Walking Distance Following Lumbar Spine Surgery.

WHAT IS KNOWN AND OBJECTIVE: The opioid doses on post-operative day 1 (POD1) is a major predictor of recovery in patients following lumbar spine surgery (LSS). However, the opioid doses vary widely in clinical practice. Thus, the objective of this study was to explore the associations between opioid doses on POD1, pain and function during a hospital stay in patients following LSS. METHODS: This study used medical records of patients who underwent LSS between January 2007 and March 2018. The patients were divided into three groups (high, medium and low dose) according to the amount of opioid (oral morphine equivalents; OME) taken on POD1. A propensity score matching across the three groups was performed to account for main confounding factors related to the opioid dose, pain intensity and gait distance, which identified 114 matched patients in each group. The difference of pain intensity and gait distance between the groups on POD1 was analysed. RESULTS: The OME in each group on POD1 was 168.75 ± 69.50 mg (high), 65.92 ± 13.28 mg (medium) and 16.90 ± 9.80 mg (low) (P < .0001). Pain intensity on the postoperative day 2 (POD2) and 3 (POD3) was not different between the groups (P > .05). Gait distance on POD2 and POD3 was different between the groups but did not reach the adjusted statistically significant level of 0.017: high (170.3 ± 152.77 feet) versus medium (247.57 ± 216.65 feet) dose on POD2 (P = .04); high (179.31 ± 135.722 feet) versus low (230.94 ± 145.74 feet) dose on POD3 (P = .03); and medium (196.98 ± 159.42 feet) versus low (261.00 ± 161.03 feet) dose on POD3 (P = .09). WHAT IS NEW AND CONCLUSION: The findings indicated that high dose opioids on POD1 did not translate into better outcomes of pain and gait in patients following LSS. In fact, patients in medium and low dose groups walked a greater distance on POD2 and POD3. Use of a functional outcome such as gait should be considered to optimize opioid dose effects.

Patient vs. Community Engagement: Emerging Issues.

BACKGROUND: The value proposition of including patients at each step of the research process is that patient perspectives and preferences can have a positive impact on both the science and the outcomes of comparative effectiveness research. How to accomplish engagement and the extent to which approaches to community engagement inform strategies for effective patient engagement need to be examined to address conducting and accelerating comparative effectiveness research. OBJECTIVES: To examine how various perspectives and diverse training lead investigators and patients to conflicting positions on how best to advance patient engagement. RESEARCH DESIGN: Qualitative methods were used to collect perspectives and models of engagement from a diverse group of patients, researchers and clinicians. The project culminated with a workshop involving these stakeholders. The workshop used a novel approach, combining World Café and Future Search techniques, to compare and contrast aspects of patient engagement and community engagement. SUBJECTS: Participants included patients, researchers, and clinicians. MEASURES: Group and workshop discussions provided the consensus on topics related to patient and community engagement. RESULTS: Participants developed and refined a framework that compares and contrasts features associated with patient and community engagement. CONCLUSIONS: Although patient and community engagement may share a similar approach to engagement based on trust and mutual benefit, there may be distinctive aspects that require a unique lexicon, strategies, tactics, and activities.

Causal risk factor discovery for severe acute kidney injury using electronic health records.

BACKGROUND: Acute kidney injury (AKI), characterized by abrupt deterioration of renal function, is a common clinical event among hospitalized patients and it is associated with high morbidity and mortality. AKI is defined in three stages with stage-3 being the most severe phase which is irreversible. It is important to effectively discover the true risk factors in order to identify high-risk AKI patients and allow better targeting of tailored interventions. However, Stage-3 AKI patients are very rare (only 0.2% of AKI patients) with a large scale of features available in EHR (1917 potential risk features), yielding a scenario unfeasible for any correlation-based feature selection or modeling method. This study aims to discover the key factors and improve the detection of Stage-3 AKI. METHODS: A causal discovery method (McDSL) is adopted for causal discovery to infer true causal relationship between information buried in EHR (such as medication, diagnosis, laboratory tests, comorbidities and etc.) and Stage-3 AKI risk. The research approach comprised two major phases: data collection, and causal discovery. The first phase is propose to collect the data from HER (includes 358 encounters and 891 risk factors). Finally, McDSL is employed to discover the causal risk factors of Stage-3 AKI, and five well-known machine learning models are built for predicting Stage-3 AKI with 10-fold cross-validation (predictive accuracy were measured by AUC, precision, recall and F-score). RESULTS: McDSL is useful for further research of EHR. It is able to discover four causal features, all selected features are medications that are modifiable. The latest research of machine learning is employed to compare the performance of prediction, and the experimental result has verified the selected features are pivotal. CONCLUSIONS: The features selected by McDSL, which enable us to achieve significant dimension reduction without sacrificing prediction accuracy, suggesting potential clinical use such as helping physicians develop better prevention and treatment strategies.

Lumbar Spine Surgeries and Medication Usage During Hospital Stay: One-Center Perspective.

Background: Pain after spine surgery is usually managed with opioid and nonopioids. The rate of lumbar spine surgeries (LSS) is rising, but current practices on LSS are not known. A current trend in LSS and medication usage by age group is needed to gain a better understanding of how LSS and its pain management vary by age. Objective: The aim of this study was to report current practices of LSS of discectomy, laminectomy, and fusion in patients aged 18 and older and to gain an understanding of medication use for management of LSS. Methods: This retrospective study analyzed data of the University of Kansas Medical Center from 2007 to 2014 of patients (>18 years of age) undergoing laminectomy, discectomy, and fusion. Results: A total of 19 463 patients underwent LSS between 2007 and 2014 at Kansas University hospital. For the purpose of this study, 3115 patients' medical records were observed. A 50% increase in LSS between 2007 and 2014 was noted. Specifically, more than 2-fold increase in LSS was observed in patients aged 65 years and older. Among those aged 65 years and older, laminectomy was the most commonly performed surgery (69.6%) while discectomy was the most common surgery performed among those aged 18 to 34 (82.9%) and those aged 35 to 44 (72%). The medication use also increased with a highest usage in opioids alone (55%), followed by opioids combined with other analgesics (42.7%), regardless of lumbar surgery type or age. Conclusion: The information of increase in both LSS and the medication usage over the 7 years can be used to gain a better understanding of quality, expenditure, and outcomes following LSS. This knowledge may help health care providers plan patient care and rehabilitation services for older adults, as the trajectory of lumbar spine surgery is likely to rise with growing prevalence of older adults. The information regarding increased opioid utilization may also help clinicians to refine opioid usage and consider alternative approaches to manage acute postoperative pain, in light of the current concerns related to overutilization of opioids.

Fitness-for-use of Retrospective Multicenter Electronic Health Records to Conduct Outcome Analysis for Pediatric Ulcerative Colitis.

The use of electronic health records has garnered interest as an approach for conducting innovative outcome research and producing real-world evidence at a reduced cost compared to traditional clinical trials. The study aimed to evaluate the utility of deidentified EHR data from a multicenter research network to identify characteristics associated with treatment escalation (TE) in newly diagnosed pediatric ulcerative colitis patients. EHR data (01/2010-12/2021) from 13 Midwest healthcare systems (Greater Plains Collaborative) were collected for pediatric ulcerative colitis patients. We identified standard treatments, excluded missing initial therapy data, and analyzed the TE and time-to-TE outcomes. The clinical and laboratory characteristics at baseline were extracted. Logistic and Cox models were used, and the missing risk factors were imputed. Machine-learning Bayesian additive regression trees were also utilized to create partial dependence plots for assessing the associations between risk factors and clinical outcomes. A total of 502 eligible pediatric patients (aged 4-17 years) who initiated standard treatment were identified. Among them, 205 out of 502 (41%) experienced TE, with a median (P25, P75) duration of 63 (9, 237) days after the initial treatment. Additionally, 20 out of 509 (4%) patients underwent colectomy (COL) with a median (P25, P75) duration of 80 (3, 205) days. Both multivariable logistic regression and Cox proportional hazards regression demonstrated moderate discriminative power in predicting TE and time-to-TE, respectively. Common positive predictors for both TE and time-to-TE included a high monocyte proportion and elevated platelet counts. Conversely, BMI z-score, albumin, hemoglobin levels, and lymphocyte proportion were negatively associated with both TE and time-to-TE. This study demonstrates that multicenter EHR data can be used to identify a trial-comparable study sample of potentially larger size and to identify clinically meaningful endpoints for conducting outcome analysis and generating real-world evidence.

Positive Airway Pressure Therapy Predicts Lower Mortality and Major Adverse Cardiovascular Events Incidence in Medicare Beneficiaries with Obstructive Sleep Apnea.

Background: Obesity is associated with obstructive sleep apnea (OSA) and cardiovascular risk. Positive airway pressure (PAP) is the first line treatment for OSA, but evidence on its beneficial effect on major adverse cardiovascular events (MACE) prevention is limited. Using claims data, the effects of PAP on mortality and incidence of MACE among Medicare beneficiaries with OSA were examined. Methods: A cohort of Medicare beneficiaries with ≥2 distinct OSA claims was defined from multi-state, state-wide, multi-year (2011-2020) Medicare fee-for-service claims data. Evidence of PAP initiation and utilization was based on PAP claims after OSA diagnosis. MACE was defined as a composite of myocardial infarction, heart failure, stroke, or coronary revascularization. Doubly robust Cox proportional hazards models with inverse probability of treatment weights estimated treatment effects controlling for sociodemographic and clinical factors. Results: Among 888,835 beneficiaries with OSA (median age 73 years; 43.9% women; median follow-up 1,141 days), those with evidence of PAP initiation (32.6%) had significantly lower all-cause mortality (HR [95%CI]: 0.53 [0.52-0.54]) and MACE incidence risk (0.90 [0.89-0.91]). Higher quartiles of annual PAP claims were progressively associated with lower mortality (Q2: 0.84 [0.81-0.87], Q3: 0.76 [0.74-0.79], Q4: 0.74 [0.72-0.77]) and MACE incidence risk (Q2: 0.92 [0.89-0.95], Q3: 0.89 [0.86-0.91], Q4: 0.87 [0.85-0.90]). Conclusion: PAP utilization was associated with lower all-cause mortality and MACE incidence among Medicare beneficiaries with OSA. Results might inform trials assessing the importance of OSA therapy towards minimizing cardiovascular risk and mortality in older adults.

Design of a pragmatic randomized implementation effectiveness trial testing a health system wide hypertension program for older adults.

Hypertension control remains poor. Multiple barriers at the level of patients, providers, and health systems interfere with implementation of hypertension guidelines and effective lowering of BP. Some strategies such as self-measured blood pressure (SMBP) and remote management by pharmacists are safe and effectively lower BP but have not been effectively implemented. In this study, we combine such evidence-based strategies to build a remote hypertension program and test its effectiveness and implementation in large health systems. This randomized, controlled, pragmatic type I hybrid implementation effectiveness trial will examine the virtual collaborative care clinic (vCCC), a hypertension program that integrates automated patient identification, SMBP, remote BP monitoring by trained health system pharmacists, and frequent patient-provider communication. We will randomize 1000 patients with uncontrolled hypertension from two large health systems in a 1:1 ratio to either vCCC or control (usual care with education) groups for a 2-year intervention. Outcome measures including BP measurements, cognitive function, and a symptom checklist will be completed during study visits. Other outcome measures of cardiovascular events, mortality, and health care utilization will be assessed using Medicare data. For the primary outcome of proportion achieving BP control (defined as systolic BP < 130 mmHg) in the two groups, we will use a generalized linear mixed model analysis. Implementation outcomes include acceptability and feasibility of the program. This study will guide implementation of a hypertension program within large health systems to effectively lower BP.

Electronic health record data quality variability across a multistate clinical research network.

Background: Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. Methods: To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. Results: The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Conclusion: Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.

Medications and conditions associated with weight loss in patients prescribed semaglutide based on real-world data.

OBJECTIVE: Approved by the Food and Drug Administration (FDA) in 2017 for diabetes and in 2021 for weight loss, semaglutide has seen widespread use among individuals who aim to lose weight. The aim of this study was to evaluate weight loss and the influence of clinical factors on semaglutide patients in real-world clinical practice. METHODS: Using data from 10 health systems within the Greater Plains Collaborative (a PCORnet Clinical Research Network), nearly 4000 clinical factors encompassing demographic, diagnosis, and prescription information were extracted for semaglutide patients. A gradient-boosting, machine-learning classifier was developed for weight-loss prediction and identification of the most impactful factors via SHapley Additive exPlanations (SHAP) value extrapolation. RESULTS: A total of 3555 eligible patients (539 of whom were observed 52 weeks following exposure) from March 2017 to April 2022 were studied. On average, individuals lost 4.44% (male individuals, 3.66%; female individuals, 5.08%) of their initial weight. History of diabetes mellitus diagnosis was associated with less weight loss, whereas prediabetes and linaclotide use were associated with more pronounced weight loss. CONCLUSIONS: Weight loss in patients prescribed semaglutide from real-world evidence was strong but attenuated compared with previous clinical trials. Machine-learning analysis of electronic health record data identified factors that warrant further research and consideration when tailoring weight-loss therapy.

Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data.

The integration of electronic health records (EHRs) with social determinants of health (SDoH) is crucial for population health outcome research, but it requires the collection of identifiable information and poses security risks. This study presents a framework for facilitating de-identified clinical data with privacy-preserved geocoded linked SDoH data in a Data Lake. A reidentification risk detection algorithm was also developed to evaluate the transmission risk of the data. The utility of this framework was demonstrated through one population health outcomes research analyzing the correlation between socioeconomic status and the risk of having chronic conditions. The results of this study inform the development of evidence-based interventions and support the use of this framework in understanding the complex relationships between SDoH and health outcomes. This framework reduces computational and administrative workload and security risks for researchers and preserves data privacy and enables rapid and reliable research on SDoH-connected clinical data for research institutes.

Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.

The types of clinical notes in electronic health records (EHRs) are diverse and it would be great to standardize them to ensure unified data retrieval, exchange, and integration. The LOINC Document Ontology (DO) is a subset of LOINC that is created specifically for naming and describing clinical documents. Despite the efforts of promoting and improving this ontology, how to efficiently deploy it in real-world clinical settings has yet to be explored. In this study we evaluated the utility of LOINC DO by mapping clinical note titles collected from five institutions to the LOINC DO and classifying the mapping into three classes based on semantic similarity between note titles and LOINC DO codes. Additionally, we developed a standardization pipeline that automatically maps clinical note titles from multiple sites to suitable LOINC DO codes, without accessing the content of clinical notes. The pipeline can be initialized with different large language models, and we compared the performances between them. The results showed that our automated pipeline achieved an accuracy of 0.90. By comparing the manual and automated mapping results, we analyzed the coverage of LOINC DO in describing multi-site clinical note titles and summarized the potential scope for extension.

Mapping Clinical Documents to the Logical Observation Identifiers, Names and Codes (LOINC) Document Ontology using Electronic Health Record Systems Structured Metadata.

As Electronic Health Record (EHR) systems increase in usage, organizations struggle to maintain and categorize clinical documentation so it can be used for clinical care and research. While prior research has often employed natural language processing techniques to categorize free text documents, there are shortcomings relative to computational scalability and the lack of key metadata within notes' text. This study presents a framework that can allow institutions to map their notes to the LOINC document ontology using a Bag of Words approach. After preliminary manual value- set mapping, an automated pipeline that leverages key dimensions of metadata from structured EHR fields aligns the notes with the dimensions of the document ontology. This framework resulted in 73.4% coverage of EHR documents, while also mapping 132 million notes in less than 2 hours; an order of magnitude more efficient than NLP based methods.

Tailoring Rule-Based Data Quality Assessment to the Patient-Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).

Individual researchers and research networks have developed and applied different approaches to assess the data quality of electronic health record (EHR) data. A previously published rules-based method to evaluate the data quality of EHR data provides deeper levels of data quality analysis. To examine the effectiveness and generalizability of the rule-based framework, we reprogrammed and translated published rule templates to operate against the PCORnet Common Data Model and executed them against a database for a single center of the Greater Plains Collaborative (GPC) PCORnet Clinical Research Network. The framework detected additional data errors and logical inconsistencies not revealed by current data quality procedures. Laboratory and medication data were more vulnerable to errors. Hemolyzed samples in the emergency department and metformin prescribing in ambulatory clinics are further described to illustrate application of specific rule-based findings by researchers to engage their health systems in evaluating healthcare delivery and clinical quality concerns.

Natural Language Processing for Enterprise-scale De-identification of Protected Health Information in Clinical Notes.

Patient privacy is a major concern when allowing data sharing and the flow of health information. Hence, de-identification and anonymization techniques are used to ensure the protection of patient health information while supporting the secondary uses of data to advance the healthcare system and improve patient outcomes. Several de-identification tools have been developed for free-text, however, this research focuses on developing notes de-identification and adjudication framework that has been tested for i2b2 searches. The aim is to facilitate clinical notes research without an additional HIPAA approval process or consent by a clinician or patient especially for narrative free-text notes such as physician and nursing notes. In this paper, we build a scalable, accurate, and maintainable pipeline for notes de-identification utilizing the natural language processing and REDCap database as a method of adjudication verification. The system is deployed at an enterprise-scale where researchers can search and visualize over 45 million de-identified notes hosted in an i2b2 instance.