scholarly journals How accurate is the medical record? A comparison of the physician’s note with a concealed audio recording in unannounced standardized patient encounters

2020 ◽  
Vol 27 (5) ◽  
pp. 770-775 ◽  
Author(s):  
Saul J Weiner ◽  
Shiyuan Wang ◽  
Brendan Kelly ◽  
Gunjan Sharma ◽  
Alan Schwartz
Keyword(s):  
Medical Record ◽  
Resource Utilization ◽  
Gold Standard ◽  
Quality Care ◽  
Error Rates ◽  
Small Sample ◽  
Department Of Veterans Affairs ◽  
Audio Recording ◽  
Extensive Documentation ◽  
Standardized Patient Encounters

Abstract Objectives Accurate documentation in the medical record is essential for quality care; extensive documentation is required for reimbursement. At times, these 2 imperatives conflict. We explored the concordance of information documented in the medical record with a gold standard measure. Materials and Methods We compared 105 encounter notes to audio recordings covertly collected by unannounced standardized patients from 36 physicians, to identify discrepancies and estimate the reimbursement implications of billing the visit based on the note vs the care actually delivered. Results There were 636 documentation errors, including 181 charted findings that did not take place, and 455 findings that were not charted. Ninety percent of notes contained at least 1 error. In 21 instances, the note justified a higher billing level than the gold standard audio recording, and in 4, it underrepresented the level of service (P = .005), resulting in 40 level 4 notes instead of the 23 justified based on the audio, a 74% inflated misrepresentation. Discussion While one cannot generalize about specific error rates based on a relatively small sample of physicians exclusively within the Department of Veterans Affairs Health System, the magnitude of the findings raise fundamental concerns about the integrity of the current medical record documentation process as an actual representation of care, with implications for determining both quality and resource utilization. Conclusion The medical record should not be assumed to reflect care delivered. Furthermore, errors of commission—documentation of services not actually provided—may inflate estimates of resource utilization.

scholarly journals A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 234 ◽  
Author(s):  
Sumet Mehta ◽  
Xiangjun Shen ◽  
Jiangping Gou ◽  
Dejiao Niu
Keyword(s):  
Small Sample Size ◽  
Classification Performance ◽  
Error Rates ◽  
Small Sample ◽  
Harmonic Mean ◽  
Local Means ◽  
Local Mean ◽  
Real World Datasets ◽  
Nearest Neighbour Classifier ◽  
Query Sample

The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.

scholarly journals Cluster Wild Bootstrapping to Handle Dependent Effect Sizes in Meta-Analysis with a Small Number of Studies

2021 ◽  
Author(s):  
Megha Joshi ◽  
James E Pustejovsky ◽  
S. Natasha Beretvas
Keyword(s):  
Effect Size ◽  
Type I Error ◽  
Meta Analysis ◽  
Error Rates ◽  
Small Sample ◽  
Type I ◽  
Hypothesis Tests ◽  
Type I Error Rates ◽  
Meta Analyses ◽  
Small Sample Correction

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

scholarly journals Analysis of Healthcare Resource Utilization and Costs after the Initiation of Biologic Treatment in Patients with Ulcerative Colitis and Crohn’s Disease

10.36469/9791 ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 96-112 ◽  
Author(s):  
Sue Perera ◽  
Shibing Yang ◽  
Marni Stott-Miller ◽  
Joanne Brady
Keyword(s):  
Ulcerative Colitis ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Resource Utilization ◽  
Healthcare Resource ◽  
Healthcare Resource Utilization ◽  
Small Sample ◽  
Outpatient Visits ◽  
Ed Visits

Background: This retrospective cohort study aimed to describe and quantify healthcare resource utilization and costs for patients with ulcerative colitis (UC) and Crohn’s disease (CD) following initiation of biologic therapy. Methods: Resource utilization and costs were analyzed at baseline and 1- and 2-years after initiating a biologic. Data were extracted from a US administrative health insurance claims database for adults ≥18 years. Eligible patients were continuously enrolled in a health plan with medical and pharmacy benefits for ≥12 months prior to, and 12 months (primary analysis) or 24 months (secondary analysis) after index date (biologic initiation). Results: In total, 4864 and 2692 patients with UC, and 8910 and 5227 patients with CD were identified in the 1- and 2-year follow-up cohorts, respectively. Of 1-year follow-up cohort patients, 45% received the same biologic initiated at index for ≥1 year. Infliximab and adalimumab were the most commonly initiated biologics in patients with UC or CD. The highest proportion of patients who continued with the same biologic after 1-and 2-years had initiated therapy with infliximab for both indications (although at the 1-year follow-up for CD, the highest proportion continued to use natalizumab, but this was a small sample [n=15]). Generally, the proportion of patients having inpatient admissions and emergency department (ED) visits decreased after receiving the same biologic for 1 year compared with baseline, although the proportion having outpatient visits did not change. Mean per patient all-cause costs for inpatient hospitalizations, ED visits and outpatient visits decreased for patients with UC or CD who received the same biologic for 1 year, while mean pharmacy costs per patient increased. Conclusions; This descriptive analysis shows that although biologics effectively reduced inpatient and ED resource utilization and corresponding costs in patients with UC and CD, total management costs increased, driven by increased pharmacy costs.

scholarly journals The STORK dataset: Linked midwifery and delivery records of the mothers and index children in the Avon Longitudinal Study of Parents and Children (ALSPAC)

2020 ◽  
Vol 5 ◽  
pp. 229
Author(s):  
Mark Mummé ◽  
Andy Boyd ◽  
Jean Golding ◽  
John Macleod
Keyword(s):  
Longitudinal Study ◽  
Gold Standard ◽  
Small Sample ◽  
Birth Cohort Study ◽  
Future Research ◽  
Record System ◽  
Standard Data ◽  
Parents And Children ◽  
Sample Spot ◽  
Avon Longitudinal Study

This data note describes the linked antenatal and delivery records of the mothers and index children of the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort study. These records were extracted from the computerised maternity record system ‘STORK’ used by the two largest NHS trusts in the study catchment area. The STORK database was designed to be populated by midwives and other health professionals during a woman’s pregnancy and shortly after the baby’s birth. These early computer records were initiated in the early 1990s, shortly before the start of enrolment to ALSPAC. At this time the use of electronic medical record systems such as ‘STORK’ was very new, the accuracy of the records has been questioned and little contemporary detailed documentation is available. Small sample spot checks on the accuracy of the information in ‘STORK’ suggests extensive missingness and differences against gold-standard fieldworker abstracted information in some variables; yet high levels of completeness and agreement with gold-standard data in others. Software code was created using STATA (StataCorp LLC) to transform the original CSV (comma-separated values) files into a cohesive and consistent format which was reviewed for data-completeness for its potential use in future research. The cleaned ‘STORK’ records provide health, social and maternity data from the very earliest period of the ALSPAC study in an easily accessible format, which is particularly useful when other sources of data are missing.

scholarly journals Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-188
Author(s):  
Yoshifumi Ukyo ◽  
Hisashi Noma ◽  
Kazushi Maruo ◽  
Masahiko Gosho
Keyword(s):  
Clinical Trial ◽  
Repeated Measures ◽  
Type I Error ◽  
Mixed Effects ◽  
Error Rates ◽  
Small Sample ◽  
Mixed Effects Model ◽  
Model Complexity ◽  
Type I ◽  
Inference Methods

The mixed-effects model for repeated measures (MMRM) approach has been widely applied for longitudinal clinical trials. Many of the standard inference methods of MMRM could possibly lead to the inflation of type I error rates for the tests of treatment effect, when the longitudinal dataset is small and involves missing measurements. We propose two improved inference methods for the MMRM analyses, (1) the Bartlett correction with the adjustment term approximated by bootstrap, and (2) the Monte Carlo test using an estimated null distribution by bootstrap. These methods can be implemented regardless of model complexity and missing patterns via a unified computational framework. Through simulation studies, the proposed methods maintain the type I error rate properly, even for small and incomplete longitudinal clinical trial settings. Applications to a postnatal depression clinical trial are also presented.

Testing the Integrative Quality Care Assessment Tool (INQUAT)

2019 ◽  
Vol 33 (1) ◽  
pp. 120-144
Author(s):  
Rebecca Amati ◽  
Tommaso Bellandi ◽  
Amer A. Kaissi ◽  
Annegret F. Hannawa
Keyword(s):  
Quality Improvement ◽  
Continuity Of Care ◽  
Assessment Tool ◽  
Small Sample Size ◽  
Quality Care ◽  
Small Sample ◽  
Content Type ◽  
Care Assessment ◽  
Quality Aspects ◽  
The Us

Purpose Identifying the factors that contribute or hinder the provision of good quality care within healthcare institutions, from the managers’ perspective, is important for the success of quality improvement initiatives. The purpose of this paper is to test the Integrative Quality Care Assessment Tool (INQUAT) that was previously developed with a sample of healthcare managers in the USA. Design/methodology/approach Written narratives of 69 good and poor quality care episodes were collected from 37 managers in Italy. A quantitative content analysis was conducted using the INQUAT coding scheme, to compare the results of the US-based study to the new Italian sample. Findings The core frame of the INQUAT was replicated and the meta-categories showed similar distributions compared to the US data. Structure (i.e. organizational, staff and facility resources) covered 8 percent of all the coded units related to quality aspects; context (i.e. clinical factors and patient factors) 10 percent; process (i.e. communication, professional diligence, timeliness, errors and continuity of care) 49 percent; and outcome (i.e. process- and short-term outcomes) 32 percent. However, compared to the US results, Italian managers attributed more importance to different categories’ subcomponents, possibly due to the specificity of each sample. For example, professional diligence, errors and continuity of care acquired more weight, to the detriment of communication. Furthermore, the data showed that process subcomponents were associated to perceived quality more than outcomes. Research limitations/implications The major limitation of this investigation was the small sample size. Further studies are needed to test the reliability and validity of the INQUAT. Originality/value The INQUAT is proposed as a tool to systematically conduct in depth analyses of successful and unsuccessful healthcare events, allowing to better understand the factors that contribute to good quality and to identify specific areas that may need to be targeted in quality improvement initiatives.

On the use of chi-square analyses in studies of resource utilization

1991 ◽  
Vol 21 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Dennis E. Jelinski
Keyword(s):  
Resource Utilization ◽  
Goodness Of Fit ◽  
Small Sample ◽  
Homogeneity Test ◽  
Type I ◽  
Sample Sizes ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Test Of Homogeneity ◽  
Small Sample Sizes

Chi-square (χ2) tests are analytic procedures that are often used to test the hypothesis that animals use a particular food item or habitat in proportion to its availability. Unfortunately, several sources of error are common to the use of χ2 analysis in studies of resource utilization. Both the goodness-of-fit and homogeneity tests have been incorrectly used interchangeably when resource availabilities are estimated or known apriori. An empirical comparison of the two methods demonstrates that the χ2 test of homogeneity may generate results contrary to the χ2 goodness-of-fit test. Failure to recognize the conservative nature of the χ2 homogeneity test, when "expected" values are known apriori, may lead to erroneous conclusions owing to the increased possibility of committing a type II error. Conversely, proper use of the goodness-of-fit method is predicated on the availability of accurate maps of resource abundance, or on estimates of resource availability based on very large sample sizes. Where resource availabilities have been estimated from small sample sizes, the use of the χ2 goodness-of-fit test may lead to type I errors beyond the nominal level of α. Both tests require adherence to specific critical assumptions that often have been violated, and accordingly, these assumptions are reviewed here. Alternatives to the Pearson χ2 statistic are also discussed.

scholarly journals Diagnostic Classifications and Resource Utilization of Decedents Served by the Department of Veterans Affairs

2007 ◽  
Vol 10 (5) ◽  
pp. 1137-1145 ◽  
Author(s):  
Sonia A. Duffy ◽  
Laurel A. Copeland ◽  
Faith P. Hopp ◽  
Robert J. Zalenski
Keyword(s):  
Resource Utilization ◽  
Veterans Affairs ◽  
Department Of Veterans Affairs

scholarly journals Cooperative Parent-Mediated Therapy in Children with Fragile X Syndrome and Williams Beuren Syndrome: A Pilot RCT Study of a Transdiagnostic Intervention-Preliminary Data

2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Paolo Alfieri ◽  
Francesco Scibelli ◽  
Laura Casula ◽  
Simone Piga ◽  
Eleonora Napoli ◽  
...  
Keyword(s):  
Fragile X Syndrome ◽  
Preliminary Data ◽  
Gold Standard ◽  
Fragile X ◽  
Autism Spectrum ◽  
Small Sample ◽  
Control Group ◽  
Pilot Rct ◽  
Experimental Group ◽  
Communicative Skill

Children with fragile X syndrome and William Beuren syndrome share several socio-communicative deficits. In both populations, around 30/35% of individuals meets criteria for autism spectrum disorder on gold standard instruments. Notwithstanding, few studies have explored feasibility and validity of therapy for socio-communicative deficits in individuals with these genetic conditions. In this study, we present preliminary data on a pilot RCT aimed to verify the effectiveness of cooperative parent-mediated therapy for socio-communicative deficits in a transdiagnostic perspective in a small sample of 12 participants. Our preliminary data showed that the experimental group had significant improvement in one socio-communicative skill (responsivity) and in clinical global impression, while the control group in an adaptive measure of socialization and word production. Implications of these results are then discussed.

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