Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives. METHOD: We used data from the Medical University of South Carolina (MUSC) Research Data Warehouse. Patients 18 years-of-age or older who were diagnosed with prostate cancer between January 1, 2014 and May 31, 2017 were eligible for this study. NLP pipelines identifying social isolation were developed via extraction of notes on progress, history and physical, consult, emergency department provider, telephone encounter, discharge summary, plan of care, and radiation oncology. Of 4195 eligible prostate cancer patients, we randomly sampled 3138 patients (75%) as a training dataset. The remaining 1057 patients (25%) were used as a test dataset to evaluate NLP algorithm performance. Standard performance measures for the NLP algorithm, including precision, recall, and F-measure, were assessed by expert manual review using the test dataset. RESULTS: A total of 55,516 clinical notes from 3138 patients were included to develop the lexicon and NLP pipelines for social isolation. Of those, 35 unique patients (1.2%) had social isolation mention(s) in 217 notes. Among 24 terms relevant to social isolation, the most prevalent were "lack of social support," "lonely," "social isolation," "no friends," and "loneliness". Among 1057 patients in the test dataset, 17 patients (1.6%) were identified as having social isolation mention(s) in 40 clinical notes. Manual review identified four false positive mentions of social isolation and one false negatives in 154 notes from randomly selected 52 controls. The NLP pipeline demonstrated 90% precision, 97% recall, and 93% F-measure. The major reasons for a false positive included the ambiguities of the experiencer of social isolation, negation, and alternate meaning of words. CONCLUSIONS: Our NLP algorithms demonstrate a highly accurate approach to identify social isolation.

Correction to: Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

Following publication of the original article [1], the authors reported an error in one of the authors' names.

Depression among people with type 2 diabetes mellitus, US National Health and Nutrition Examination Survey (NHANES), 2005-2012.

BACKGROUND: Depression in people with diabetes can result in increased risk for diabetes-related complications. The prevalence of depression has been estimated to be 17.6 % in people with type 2 diabetes mellitus (T2DM), based on studies published between 1980 and 2005. There is a lack of more recent estimates of depression prevalence among the US general T2DM population. METHODS: The present study used the US National Health and Nutrition Examination Survey (NHANES) 2005-2012 data to provide an updated, population-based estimate for the prevalence of depression in people with T2DM. NHANES is a cross-sectional survey of a nationally representative sample of the civilian, non-institutionalized US population. Starting from 2005, the Patient Health Questionnaire (PHQ-9) was included to measure signs and symptoms of depression. We defined PHQ-9 total scores ≥ 10 as clinically relevant depression (CRD), and ≥ 15 as clinically significant depression (CSD). Self-reported current antidepressant use was also combined to estimate overall burden of depression. Predictors of CRD and CSD were investigated using survey logistic regression models. RESULTS: A total of 2182 participants with T2DM were identified. The overall prevalence of CRD and CSD among people with T2DM is 10.6 % (95 % confidence interval (CI) 8.9-12.2 %), and 4.2 % (95 % CI 3.4-5.1 %), respectively. The combined burden of depressive symptoms and antidepressants may be as high as 25.4 % (95 % CI 23.0-27.9 %). Significant predictors of CRD include age (younger than 65), sex (women), income (lower than 130 % of poverty level), education (below college), smoking (current or former smoker), body mass index (≥30 kg/m(2)), sleep problems, hospitalization in the past year, and total cholesterol (≥200 mg/dl). Significant predictors of CSD also include physical activity (below guideline) and cardiovascular diseases. CONCLUSIONS: The prevalence of CRD and CSD among people with T2DM in the US may be lower than in earlier studies, however, the burden of depression remains high. Further research with longitudinal follow-up for depression in people with T2DM is needed to understand real world effectiveness of depression management.

Data for drugs available through low-cost prescription drug programs are available through pharmacy benefit manager and claims data.

BACKGROUND: Observational data are increasingly being used for pharmacoepidemiological, health services and clinical effectiveness research. Since pharmacies first introduced low-cost prescription programs (LCPP), researchers have worried that data about the medications provided through these programs might not be available in observational data derived from administrative sources, such as payer claims or pharmacy benefit management (PBM) company transactions. METHOD: We used data from the Indiana Network for Patient Care to estimate the proportion of patients with type 2 diabetes to whom an oral hypoglycemic agent was dispensed. Based on these estimates, we compared the proportions of patients who received medications from chains that do and do not offer an LCPP, the proportion trend over time based on claims data from a single payer, and to proportions estimated from the Medical Expenditure Panel Survey (MEPS). RESULTS: We found that the proportion of patients with type 2 diabetes who received oral hypoglycemic medications did not vary based on whether the chain that dispensed the drug offered an LCPP or over time. Additionally, the rates were comparable to those estimated from MEPS. CONCLUSION: Researchers can be reassured that data for medications available through LCPPs continue to be available through administrative data sources.

Automatically identifying opioid use disorder in non-cancer patients on chronic opioid therapy.

Background: Using the International Classification of Diseases (ICD) codes alone to record opioid use disorder (OUD) may not completely document OUD in the electronic health record (EHR). We developed and evaluated natural language processing (NLP) approaches to identify OUD from the clinal note. We explored the concordance between ICD-coded and NLP-identified OUD.Methods: We studied EHRs from 13,654 (female: 8223; male: 5431) adult non-cancer patients who received chronic opioid therapy (COT) and had at least one clinical note between 2013 and 2018. Of eligible patients, we randomly selected 10,218 (75%) patients as the training set and the remaining 3436 patients (25%) as the test dataset for NLP approaches.Results: We generated 539 terms representing OUD mentions in clinical notes (e.g., "opioid use disorder," "opioid abuse," "opioid dependence," "opioid overdose") and 73 terms representing OUD medication treatments. By domain expert manual review for the test dataset, our NLP approach yielded high performance: 98.5% for precision, 100% for recall, and 99.2% for F-measure. The concordance of these NLP and ICD identified OUD was modest (Kappa = 0.63).Conclusions: Our NLP approach can accurately identify OUD patients from clinical notes. The combined use of ICD diagnostic code and NLP approach can improve OUD identification.

The Impact of Censoring Drug Switching in Medication Adherence Measures of Chronic Single Ingredient Oral Drugs.

We explored how drug switching impacts adherence measures for common chronic oral medications. Switching between ingredients with the same indication was detected within a 30-day grace period. The proportion of days covered (PDC) and adherent status (cutoff 0.8) for each ingredient was calculated and compared between different censoring approaches: censoring drug switching (PDCswitch), censoring the end of dispensing (PDCend), and fixed 365-day period (PDC365). Overall, 854,380 (15.9%) patients in the Optum ClinFormatics (Optum) and 150,785 (22.0%) patients in the MarketScan Multi-state Medicaid (MDCD) had at least one switch within one year. Compared with PDC365 in Optum, PDCswitch means were higher: 0.85 vs. 0.41 for antihypertensive, 0.82 vs. 0.46 for antihyperglycemics, and 0.84 vs. 0.33 for antihyerlipidemia. Further, the percentages of adherent patients were higher: 95.8% vs. 17.9% for antihypertensive, 85.5% vs. 18.9% for antihyperglycemics, and 72.1% vs. 5.3% for antihyerlipidemia. Significant and modest changes were observed between PDCswitch and PDCend.

Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing.

Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to identify falls risk screenings documented in clinical notes of patients without coded falls risk screening data. Extracting information from 1,558 clinical notes (mainly progress notes) from 144 eligible patients, we generated a lexicon of 38 keywords relevant to falls risk screening, 26 terms for pre-negation, and 35 terms for post-negation. The NLP algorithm identified 62 (out of the 144) patients who falls risk screening documented only in clinical notes and not coded. Manual review confirmed 59 patients as true positives and 77 patients as true negatives. Our NLP approach scored 0.92 for precision, 0.95 for recall, and 0.93 for F-measure. These results support the concept of utilizing NLP to enhance healthcare quality reporting.

Changes in Body Weight Among People With Type 2 Diabetes Mellitus in the United States, NHANES 2005-2012.

PURPOSE: To understand weight loss strategies, weight changes, goals, and behaviors in people with type 2 diabetes mellitus (T2DM) and whether these differ by ethnicity. METHODS: T2DM was identified by self-reported diagnosis using the NHANES 2005-2012 data, which also included measured and self-reported current body weight and height, self-reported weight the prior year, and self-reported aspired weight. Nineteen weight loss strategies were evaluated for association with ≥5% weight loss or weight gain versus <5% weight change. RESULTS: Among people with T2DM, 88.0% were overweight/obese (body mass index [BMI] ≥25 kg/m(2)) in the prior year and 86.1% the current year. About 60% of the overweight/obese took weight loss actions, mostly using diet-related methods with average weight lost <5%. Two most "effective" methods reported (smoking, taking laxatives/vomiting) are also potentially most harmful. Similar BMI distributions but different goals and behaviors about weight and weight loss were observed across ethnicity. Only physical activity meeting the recommended level and changing eating habits were consistently associated with favorable and statistically significant weight change. CONCLUSIONS: Weight management in T2DM is an ongoing challenge, regardless of ethnicity/race. Among overweight/obese T2DM subjects, recommended level of physical activity and changing eating habits were associated with statistically significant favorable weight change.

Nonadherence to Oral Antihyperglycemic Agents: Subsequent Hospitalization and Mortality among Patients with Type 2 Diabetes in Clinical Practice.

Using real-world clinical data from the Indiana Network for Patient Care, we analyzed the associations between non-adherence to oral antihyperglycemic agents (OHA) and subsequent diabetes-related hospitalization and all-cause mortality for patients with type 2 diabetes. OHA adherence was measured by the annual proportion of days covered (PDC) for 2008 and 2009. Among 24,067 eligible patients, 35,507 annual PDCs were formed. Over 90% (n=21,798) of the patients had a PDC less than 80%. In generalized linear mixed model analyses, OHA non-adherence is significantly associated with diabetes related hospitalizations (OR: 1.2; 95% CI [1.1,1.3]; p<0.0001). Older patients, white patients, or patients who had ischemic heart disease, stroke, or renal disease had higher odds of hospitalization. Similarly, OHA non-adherence increased subsequent mortality (OR: 1.3; 95% CI [1.02, 1.61]; p<0.0001). Patient age, male gender, income and presence of ischemic heart diseases, stroke, and renal disease were also significantly associated with subsequent all-cause death.

A Comparison of Data Driven-based Measures of Adherence to Oral Hypoglycemic Agents in Medicaid Patients.

We evaluated and compared different methods for measuring adherence to Oral Antihyperglycemic Agents (OHA), based on the correlation between these measures and glycated hemoglobin A1C (HbA1c) levels in Medicaid patients with Type 2 diabetes. An observational sample of 831 Medicaid patients with Type 2 diabetes who had HbA1c test results recorded between January 1, 2001 and December 31, 2005 was identified in the Indiana Network of Patient Care (INPC). OHA adherence was measured by medication possession ratio (MPR), proportion of days covered (PDC), and the number of gaps (GAP) for 3, 6, and 12-month intervals prior to the HbA1c test date. All three OHA adherence measurements showed consistent and significant correlation with HbA1c level. The 6-month PDC showed the strongest association with HbA1c levels in both unadjusted (-1.07, P<0.0001) and adjusted (-1.12, P<0.0001) models.

Evaluation of a clinical decision support algorithm for patient-specific childhood immunization.

OBJECTIVE: To evaluate the effectiveness of a clinical decision support system (CDSS) implementing standard childhood immunization guidelines, using real-world patient data from the Regenstrief Medical Record System (RMRS). METHODS: Study subjects were age 6-years or younger in 2008 and had visited the pediatric clinic on the campus of Wishard Memorial Hospital. Immunization records were retrieved from the RMRS for 135 randomly selected pediatric patients. We compared vaccine recommendations from the CDSS for both eligible and recommended timelines, based on the child's date of birth and vaccine history, to recommendations from registered nurses who routinely selected vaccines for administration in a busy inner city hospital, using the same date of birth and vaccine history. Aggregated and stratified agreement and Kappa statistics were reported. The reasons for disagreement between suggestions from the CDSS and nurses were also identified. RESULTS: For the 135 children, a total of 1215 vaccination suggestions were generated by nurses and were compared to the recommendations of the CDSS. The overall agreement rates were 81.3% and 90.6% for the eligible and recommended timelines, respectively. The overall Kappa values were 0.63 for the eligible timeline and 0.80 for the recommended timeline. Common reasons for disagreement between the CDSS and nurses were: (1) missed vaccination opportunities by nurses, (2) nurses sometimes suggested a vaccination before the minimal age and minimal waiting interval, (3) nurses usually did not validate patient immunization history, and (4) nurses sometimes gave an extra vaccine dose. CONCLUSION: Our childhood immunization CDSS can assist providers in delivering accurate childhood vaccinations.

Race and medication adherence and glycemic control: findings from an operational health information exchange.

The Central Indiana Beacon Community leads efforts for improving adherence to oral hypoglycemic agents (OHA) to achieve improvements in glycemic control for patients with type 2 diabetes. In this study, we explored how OHA adherence affected hemoglobin A1C (HbA1c) level in different racial groups. OHA adherence was measured by 6-month proportion of days covered (PDC). Of 3,976 eligible subjects, 12,874 pairs of 6-month PDC and HbA1c levels were formed between 2002 and 2008. The average HbA1c levels were 7.4% for African-Americans and 6.5% for Whites. The average 6-month PDCs were 40% for African-Americans and 50% for Whites. In mixed effect generalized linear regression analyses, OHA adherence was inversely correlated with HbA1c level for both African-Americans (-0.80, p<0.0001) and Whites (-0.53, p<0.0001). The coefficient was -0.26 (p<0.0001) for the interaction of 6-month PDC and African-Americans. Significant risk factors for OHA non-adherence were race, young age, non-commercial insurance, newly-treated status, and polypharmacy.

Facilitating Clinical Research through the Health Information Exchange: Lipid Control as an Example.

Using data from the Indiana Network of Patient Care (INPC), we analyzed long-term statin adherence patterns and their effects on low-density lipoprotein cholesterol (LDL-C) control among patients with type 2 diabetes. Statin adherence was measured by proportion of days covered (PDC) for a 6-month interval prior to each LDL-C test date. Patient demographic and clinical characteristics were used as covariates for LDL-C control and predictors for statin adherence. From 4,350 eligible subjects, 25,596 6-month PDC and LDL-C level pairs were formed between 2001 and 2009. Rates of suboptimal adherence and suboptimal LDL-C control were 68.5% and 46.6%, respectively. Positive predictors for LDL-C control included adherence to statin (OR: 1.87, p<0.0001) and older age (OR: 1.11, p=0.01). Significant risk factors for non-adherence were young age, female gender, African American race and newly-treated status. This study demonstrated the utility of a health information exchange in health outcome and clinical effectiveness research.

Implementing broad scale childhood immunization decision support as a web service.

Timely vaccinations decrease a child's risk of contracting vaccine-preventable disease and prevent disease outbreaks. Childhood immunization schedules may represent the only clinical guideline for which there is official national consensus. So an immunization clinical decision support system (CDSS) is a natural application. However, immunization schedules are complex and change frequently. Maintaining multiple CDSS's is expensive and error prone. Therefore, a practical strategy would be an immunization CDSS as a centralized web service that can be easily accessed by various electronic medical record (EMR) systems. This allows centralized maintenance of immunization guidelines. We have developed a web service, based on Miller's tabular model with modifications, which implements routine childhood immunization guidelines. This immunization web service is currently operating in the Regenstrief Institute intranet and system evaluations are ongoing. We will make this web service available on the Internet. In this paper, we describe this web service -based immunization decision support tool.

An empiric modification to the probabilistic record linkage algorithm using frequency-based weight scaling.

OBJECTIVE: To incorporate value-based weight scaling into the Fellegi-Sunter (F-S) maximum likelihood linkage algorithm and evaluate the performance of the modified algorithm. Background Because healthcare data are fragmented across many healthcare systems, record linkage is a key component of fully functional health information exchanges. Probabilistic linkage methods produce more accurate, dynamic, and robust matching results than rule-based approaches, particularly when matching patient records that lack unique identifiers. Theoretically, the relative frequency of specific data elements can enhance the F-S method, including minimizing the false-positive or false-negative matches. However, to our knowledge, no frequency-based weight scaling modification to the F-S method has been implemented and specifically evaluated using real-world clinical data. METHODS: The authors implemented a value-based weight scaling modification using an information theoretical model, and formally evaluated the effectiveness of this modification by linking 51,361 records from Indiana statewide newborn screening data to 80,089 HL7 registration messages from the Indiana Network for Patient Care, an operational health information exchange. In addition to applying the weight scaling modification to all fields, we examined the effect of selectively scaling common or uncommon field-specific values. RESULTS: The sensitivity, specificity, and positive predictive value for applying weight scaling to all field-specific values were 95.4, 98.8, and 99.9%, respectively. Compared with nonweight scaling, the modified F-S algorithm demonstrated a 10% increase in specificity with a 3% decrease in sensitivity. CONCLUSION: By eliminating false-positive matches, the value-based weight modification can enhance the specificity of the F-S method with minimal decrease in sensitivity.