Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management

Nouraei, S.A.R.; Virk, Jagdeep Singh; Hudovsky, A.; Wathen, C G; Darzi, Ara; Parsons, Darren M.

doi:10.1093/pubmed/fdv041

Cited by 37 publications

(32 citation statements)

References 25 publications

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“…In a study of coding accuracy, Nouraei et al found that in nearly 9000 patient discharge records, clinician and clinical coder concordance for acute heart failure was excellent (kappa 0.92, 95% CI 0.88–0.95). 34 Despite potential coding inaccuracies and lack of clinical detail, assessment of clinical outcomes such as mortality and readmission rates using administrative data have been validated against medical records 35 as well as prospective registries. 36,37 Data for these analyses were derived from 3 states (CA, NY, and FL); generalizability to other states requires further study.…”

Section: Discussionmentioning

confidence: 99%

All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter

Davis

Olsen

Bommarito

et al. 2017

The American Journal of Medicine

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BACKGROUND Thirty-day readmission following heart failure hospitalization impacts hospital performance measures and reimbursement. We investigated readmission characteristics and the magnitude of 30-day hospital readmissions after hospital discharge for heart failure using the Healthcare Cost and Utilization Project State Inpatient Databases (SID). METHODS Adults aged ≥ 40 years hospitalized with a primary discharge diagnosis of heart failure from 2007–2011 were identified in the California, New York, and Florida SIDs. Characteristics of patients with and without 7-, 8-30-, and 30-day readmission, and primary readmission diagnoses and risk factors for readmission were examined. RESULTS We identified 547,068 patients with mean age 74.7 years; 50.7% were female, 65.4% were white. Of 117,123 patients (21.4%) readmitted within 30 days (median 12 days), 69.7% had a non-heart failure primary readmission diagnosis. Patients with 30-day readmissions more frequently had a history of previous admission with heart failure as a secondary diagnosis, fluid and electrolyte disorders and chronic deficiency anemia. There were no significant clinical differences at baseline between those patients whose first readmission was in the first 7 days after discharge versus in the next 23 days. The most common primary diagnoses for 30-day non-heart failure readmissions were other cardiovascular conditions (14.9%), pulmonary disease (8.5%), and infections (7.7%). CONCLUSIONS In this large all-payer cohort, ~70% of 30-day readmissions were for non-heart failure causes, and the median time to readmission was 12 days. Future interventions to reduce readmissions should focus on common comorbid conditions that contribute to readmission burden.

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Section: Discussionmentioning

confidence: 99%

All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter

Davis

Olsen

Bommarito

et al. 2017

The American Journal of Medicine

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“…In head and neck cancer, differentiating between two synchronous tumours and extension of one tumour to multiple anatomical regions can be difficult. Identifying and abstracting clinical events into a coded language is prone to subjectivity, variability and error . This is not unique to the administrative data but its reduction through creating consensus between different centres could increase the generalisability of the data.…”

Section: Discussionmentioning

confidence: 99%

A stratified analysis of the perioperative outcome of 17623 patients undergoing major head and neck cancer surgery in England over 10 years: Towards an Informatics‐based Outcomes Surveillance Framework

Nouraei

Mace

Middleton

et al. 2016

Clinical Otolaryngology

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“…In recent years, some studies from the US have explored using EHR data to measure outcomes (most often in combination with claims data), including a Stanford University research group in California, testing the use of EHRs specifically for prostate cancer [45][46][47]. However, poor data quality (including accuracy of clinical coding, which is prone to subjectivity, variability and error), issues regarding privacy, ownership and access, the use of different software systems across health care settings, and the difficulty and expense of mining clinical notes may limit their application [3,45,46,48].…”

Section: Limitationsmentioning

confidence: 99%

A scoping review of core outcome sets and their ‘mapping’ onto real-world data using prostate cancer as a case study

Meregaglia

Ciani

Banks

et al. 2020

BMC Med Res Methodol

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Background: A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources. Methods: We conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A 'mapping' exercise was conducted between the COS identified and RWD routinely collected in selected European countries. Results: Eleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as 'COS for clinical trials and clinical research', 2 as 'COS for practice' and 1 as 'COS patient reported outcomes'. Recommended outcomes were mainly categorized into 'mortality and survival' (17%), 'outcomes related to neoplasm' (18%), and 'renal and urinary outcomes' (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD 'scope specification' domain but not the 'stakeholders involved' and 'consensus process' domains. About 72% overlap existed between COS and linked administrative data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups. Conclusions: This scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.

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Accuracy of clinician-clinical coder information handover following acute medical admissions: implication for using administrative datasets in clinical outcomes management

Cited by 37 publications

References 25 publications

All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter

All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter

A stratified analysis of the perioperative outcome of 17623 patients undergoing major head and neck cancer surgery in England over 10 years: Towards an Informatics‐based Outcomes Surveillance Framework

A scoping review of core outcome sets and their ‘mapping’ onto real-world data using prostate cancer as a case study

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