A comparison between physicians and computer algorithms for form CMS-2728 data reporting

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure—standardized readmission ratio—and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. NLP performance was compared for symptom detection with International Classification of Diseases (ICD)-9/10 codes and the performance of both methods were validated against manual chart review. From 1034 and 519 hemodialysis patients within BioMe and MIMIC-III databases, respectively, the most frequently identified symptoms by NLP were fatigue, pain, and nausea/vomiting. In BioMe, sensitivity for NLP (0.85 - 0.99) was higher than for ICD codes (0.09 - 0.59) for all symptoms with similar results in the BioMe validation cohort and MIMIC-III. ICD codes were significantly more specific for nausea/vomiting in BioMe and more specific for fatigue, depression, and pain in the MIMIC-III database. A majority of patients in both cohorts had four or more symptoms. Patients with more symptoms identified by NLP, ICD, and chart review had more clinical encounters. NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.

Predicting 3-Year Survival in Patients Receiving Maintenance Dialysis: An External Validation of iChoose Kidney in Ontario, Canada

BACKGROUND

Many patients with end-stage kidney disease (ESKD) do not appreciate how their survival may differ if treated with a kidney transplant compared with dialysis. A risk calculator (iChoose Kidney) developed and validated in the United States provides individualized mortality estimates for different treatment options (dialysis vs living or deceased donor kidney transplantation). The calculator can be used with patients and families to help patients make more educated treatment decisions.

OBJECTIVE

To validate the iChoose Kidney risk calculator in Ontario, Canada.

DESIGN

External validation study.

SETTING

We used several linked administrative health care databases from Ontario, Canada.

PATIENTS

We included 22 520 maintenance dialysis patients and 4505 kidney transplant recipients. Patients entered the cohort between 2004 and 2014.

MEASUREMENTS

Three-year all-cause mortality.

METHODS

We assessed model discrimination using the C-statistic. We assessed model calibration by comparing the observed versus predicted mortality risk and by using smoothed calibration plots. We used multivariable logistic regression modeling to recalibrate model intercepts using a correction factor, when appropriate.

RESULTS

In our final version of the iChoose Kidney model, we included the following variables: age (18-80 years), sex (male, female), race (white, black, other), time on dialysis (<6 months, 6-12 months, >12 months), and patient comorbidities (hypertension, diabetes, and/or cardiovascular disease). Over the 3-year follow-up period, 33.3% of dialysis patients and 6.2% of kidney transplant recipients died. The discriminatory ability was moderate (C-statistic for dialysis: 0.70, 95% confidence interval [CI]: 0.69-0.70, and C-statistic for transplant: 0.72, 95% CI: 0.69-0.75). The 3-year observed and predicted mortality estimates were comparable and even more so after we recalibrated the intercepts in 2 of our models (dialysis and deceased donor kidney transplantation). As done in the United States, we developed a Canadian Web site and an iOS application called Dialysis vs. Kidney Transplant- Estimated Survival in Ontario.

LIMITATIONS

Missing data in our databases precluded the inclusion of all variables that were in the original iChoose Kidney (ie, patient ethnicity and low albumin). We were unable to perform all preplanned analyses due to the limited sample size.

CONCLUSIONS

The original iChoose Kidney risk calculator was able to adequately predict mortality in this Canadian (Ontario) cohort of ESKD patients. After minor modifications, the predictive accuracy improved. The Dialysis vs. Kidney Transplant- Estimated Survival in Ontario risk calculator may be a valuable resource to help ESKD patients make an informed decision on pursuing kidney transplantation.