Dataset Information

The accuracy of present-on-admission reporting in administrative data.

ABSTRACT:

Objective

To test the accuracy of reporting present-on-admission (POA) and to assess whether POA reporting accuracy differs by hospital characteristics.

Data sources

We performed an audit of POA reporting of secondary diagnoses in 1,059 medical records from 48 California hospitals.

Study design

We used patient discharge data (PDD) to select records with secondary diagnoses that are powerful predictors of mortality and could potentially represent comorbidities or complications among patients who either had a primary procedure of a percutaneous transluminal coronary angioplasty or a primary diagnosis of acute myocardial infarction, community-acquired pneumonia, or congestive heart failure. We modeled the relationship between secondary diagnoses POA reporting accuracy (over-reporting and under-reporting) and hospital characteristics.

Data collection

We created a gold standard from blind reabstraction of the medical records and compared the accuracy of the PDD against the gold standard.

Principal findings

The PDD and gold standard agreed on POA reporting in 74.3 percent of records, with 13.7 percent over-reporting and 11.9 percent under-reporting. For-profit hospitals tended to overcode secondary diagnoses as present on admission (odds ratios [OR] 1.96; 95 percent confidence interval [CI] 1.11, 3.44), whereas teaching hospitals tended to undercode secondary diagnoses as present on admission (OR 2.61; 95 percent CI 1.36, 5.03).

Conclusions

POA reporting of secondary diagnoses is moderately accurate but varies by hospitals. Steps should be taken to improve POA reporting accuracy before using POA in hospital assessments tied to payments.

SUBMITTER: Goldman LE

PROVIDER: S-EPMC3393034 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Publications

The accuracy of present-on-admission reporting in administrative data.

Goldman L Elizabeth LE Chu Philip W PW Osmond Dennis D Bindman Andrew A

Health services research 20110811 6pt1

<h4>Objective</h4>To test the accuracy of reporting present-on-admission (POA) and to assess whether POA reporting accuracy differs by hospital characteristics.<h4>Data sources</h4>We performed an audit of POA reporting of secondary diagnoses in 1,059 medical records from 48 California hospitals.<h4>Study design</h4>We used patient discharge data (PDD) to select records with secondary diagnoses that are powerful predictors of mortality and could potentially represent comorbidities or complicatio ...[more]

PMID: 22092023

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Project description:Administrative data are often hampered by coding errors, absent data, and the difficulty of distinguishing pre-existing conditions from perioperative complications. We evaluated whether the introduction of the present on admission (POA) indicator improved outcome analysis of carotid endarterectomy (CEA) and carotid angioplasty and stenting (CAS) using administrative data.State inpatient databases from California (2005-2008), New York (2008), and New Jersey (2008) were used to identify patients undergoing CAS and CEA. We first analyzed morbidity data without the POA indicator, using International Classification of Diseases, Ninth Revision complication codes (eg, 997.02, iatrogenic cerebrovascular infarction or hemorrhage, postoperative stroke) and diagnosis codes (eg, 433.11, occlusion and stenosis of the carotid artery with cerebral infarction). Then, we applied the POA indicator to both diagnosis and complication codes and calculated the proportion of events that were labeled POA. Symptom status and perioperative stroke rate were compared using these coding approaches.We identified 21,639 patients who underwent CEA and 3688 patients who underwent CAS. Without the POA indicator, the complication code for stroke indicated a postoperative stroke rate of 1.4% for CEA and 2.4% for CAS. After applying the POA indicator, 54% (CEA) and 62% (CAS) of these strokes were labeled POA. These POA strokes were either preoperative or intraoperative events. Proportion of symptomatic patients ranged from 7% to 16% for CEA and from 5% to 22% for CAS. Perioperative stroke rate was the lowest in the POA method (1.1% CEA, 1.8% CAS) compared with two other methods without POA information (1.4% and 9.5% CEA and 2.4% and 16.4% CAS). Kappa indicated a poor (0.2) to fair (0.7) agreement between these approaches.Administrative data has known limitations for assignment of symptom status and nonfatal perioperative outcomes. Given the uncertain timing of POA events as preoperative vs intraoperative and its apparent underestimation of the perioperative stroke rate, the use of administrative data even with the POA indicator for symptom status and non-fatal outcomes after CEA and CAS is hazardous.

Project description:BackgroundAboriginal people are under-reported on administrative health data in Australia. Various approaches have been used or proposed to improve reporting of Aboriginal people using linked records. This cross-sectional study used self-reported Aboriginality from the NSW Patient Survey Program (PSP) as a reference standard to assess the accuracy of reporting of Aboriginal people on NSW Admitted Patient (APDC) and Emergency Department Data Collections (EDDC), and compare the accuracy of selected approaches to enhance reporting Aboriginality using linked data.MethodsTen PSP surveys were linked to five administrative health data collections, including APDC, EDDC, perinatal, and birth and death registration records. Accuracy of reporting of Aboriginality was assessed using sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) and F score for the EDDC and APDC as baseline and four enhancement approaches using linked records: "Most recent linked record", "Ever reported as Aboriginal", and two approaches using a weight of evidence, "Enhanced Reporting of Aboriginality (ERA) algorithm" and "Multi-stage median (MSM)".ResultsThere was substantial under-reporting of Aboriginality on APDC and EDDC records (sensitivities 84 and 77% respectively) with PPVs of 95% on both data collections. Overall, specificities and NPVs were above 98%. Of people who were reported as Aboriginal on the PSP, 16% were not reported as Aboriginal on any of their linked records. Record linkage approaches generally increased sensitivity, accompanied by decrease in PPV with little change in overall F score for the APDC and an increase in F score for the EDDC. The "ERA algorithm" and "MSM" approaches provided the best overall accuracy.ConclusionsWeight of evidence approaches are preferred when record linkage is used to improve reporting of Aboriginality on administrative health data collections. However, as a substantial number of Aboriginal people are not reported as Aboriginal on any of their linked records, improvements in reporting are incomplete and should be taken into account when interpreting results of any analyses. Enhancement of reporting of Aboriginality using record linkage should not replace efforts to improve recording of Aboriginal people at the point of data collection and addressing barriers to self-identification for Aboriginal people.

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Project description:BackgroundChildhood cancer relies heavily on inpatient hospital services to deliver tumor-directed therapy and manage toxicities. Hospitalizations have increased over the past decade, though not uniformly across childhood cancer diagnoses. Analysis of the reasons for admission of children with cancer could enhance comparison of resource use between cancers, and allow clinical practice data to be interpreted more readily. Such comparisons using nationwide data sources are difficult because of numerous subdivisions in the International Classification of Diseases Clinical Modification (ICD-9) system and inherent complexities of treatments. This study aimed to develop a systematic approach to classifying cancer-related admissions in administrative data into categories that reflected clinical practice and predicted resource use.MethodsWe developed a multistep algorithm to stratify indications for childhood cancer admissions in the Kids Inpatient Databases from 2003, 2006 and 2009 into clinically meaningful categories. This algorithm assumed that primary discharge diagnoses of cancer or cytopenia were insufficient, and relied on procedure codes and secondary diagnoses in these scenarios. Clinical Classification Software developed by the Healthcare Cost and Utilization Project was first used to sort thousands of ICD-9 codes into 5 mutually exclusive diagnosis categories and 3 mutually exclusive procedure categories, and validation was performed by comparison with the ICD-9 codes in the final admission indication. Mean cost, length of stay, and costs per day were compared between categories of indication for admission.ResultsA cohort of 202,995 cancer-related admissions was grouped into four categories of indication for admission: chemotherapy (N=77,791, 38%), to undergo a procedure (N=30,858, 15%), treatment for infection (N=30,380, 15%), or treatment for other toxicities (N=43,408, 21.4%). The positive predictive value for the algorithm was >95% for each category. Admissions for procedures had higher mean hospital costs, longer hospital stays, and higher costs per day compared with other admission reasons (p<0.001).ConclusionsThis is the first description of a method for grouping indications for childhood cancer admission within an administrative dataset into clinically relevant categories. This algorithm provides a framework for more detailed analyses of pediatric hospitalization data by cancer type.

Project description:BACKGROUND:The UK government is pursuing policies to improve primary care access, as many patients visit accident and emergency (A and E) departments after being unable to get suitable general practice appointments. Direct admission to hospital via a general practitioner (GP) averts A and E use, and may reduce total hospital costs. It could also enhance the continuity of information between GPs and hospital doctors, possibly improving healthcare outcomes. OBJECTIVE:To determine whether primary care access is associated with the route of emergency admission-via a GP versus via an A and E department. METHODS:Retrospective analysis of national administrative data from English hospitals for 2011-2012. Adults admitted in an emergency (unscheduled) for ?1 night via a GP or an A and E department formed the study population. The measure of primary care access-the percentage of patients able to get a general practice appointment on their last attempt-was derived from a large, nationally representative patient survey. Multilevel logistic regression was used to estimate associations, adjusting for patient and admission characteristics. RESULTS:The analysis included 2?322?112 emergency admissions (81.9% via an A and E department). With a 5?unit increase in the percentage of patients able to get a general practice appointment on their last attempt, the adjusted odds of GP admission (vs A and E admission) was estimated to increase by 15% (OR 1.15, 95% CI 1.12 to 1.17). The probability of GP admission if ?95% of appointment attempts were successful in each general practice was estimated to be 19.6%. This probability reduced to 13.6% when <80% of appointment attempts were successful. This equates to 139?673 fewer GP admissions (456?232 vs 316?559) assuming no change in the total number of admissions. Associations were consistent in direction across geographical regions of England. CONCLUSIONS:Among hospital inpatients admitted as an emergency, patients registered to more accessible general practices were more likely to have been admitted via a GP (vs an A and E department). This furthers evidence suggesting that access to general practice is related to use of emergency hospital services in England. The relative merits of the two admission routes remain unclear.

Dataset Information

The accuracy of present-on-admission reporting in administrative data.

Objective

Data sources

Study design

Data collection

Principal findings

Conclusions

Publications

The accuracy of present-on-admission reporting in administrative data.

Similar Datasets

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