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Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0., (© The Author(s) 2020. Published by Oxford University Press.)

Traditional Chinese Medicine (TCM) has been developed for several thousand years and plays a significant role in health care for Chinese people. This paper studies the problem of classifying TCM clinical records into 5 main disease categories in TCM. We explored a number of state-of-the-art deep learning models and found that the recent Bidirectional Encoder Representations from Transformers can achieve better results than other deep learning models and other state-of-the-art methods. We further utilized an unlabeled clinical corpus to fine-tune the BERT language model before training the text classifier. The method only uses Chinese characters in clinical text as input without preprocessing or feature engineering. We evaluated deep learning models and traditional text classifiers on a benchmark data set. Our method achieves a state-of-the-art accuracy 89.39% ± 0.35%, Macro F1 score 88.64% ± 0.40% and Micro F1 score 89.39% ± 0.35%. We also visualized attention weights in our method, which can reveal indicative characters in clinical text., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

As with many other aspects of the modern world, in healthcare, the explosion of data and resources opens new opportunities for the development of added-value services. Still, a number of specific conditions on this domain greatly hinders these developments, including ethical and legal issues, fragmentation of the relevant data in different locations, and a level of (meta)data complexity that requires great expertise across technical, clinical, and biological domains. We propose the Patient Dossier paradigm as a way to organize new innovative healthcare services that sorts the current limitations. The Patient Dossier conceptual framework identifies the different issues and suggests how they can be tackled in a safe, efficient, and responsible way while opening options for independent development for different players in the healthcare sector. An initial implementation of the Patient Dossier concepts in the Rbbt framework is available as open-source at https://github.com/mikisvaz and https://github.com/Rbbt-Workflows., Competing Interests: The authors have declared that no competing interests exist

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Initially, the privacy of the medical data is ensured by using the key matrix developed based on the privacy utility coefficient matrix using the chronological-Whale optimization algorithm. The privacy protected data are subjected to classification by using ACNN that performs the optimal classification using the proposed TGD algorithm. The proposed TGD algorithm is the integration of Taylor series in the gradient descent algorithm that updates the optimal weight of ACNN based on the weights in the previous iterations. The analysis using the Cleveland, Switzerland, and Hungarian dataset proves that the proposed classification strategy obtains an accuracy of 0.9252, a sensitivity of 0.8419, and a specificity of 0.8387, respectively.

The following Article discusses the extent to which the constitutional right to informational privacy protects medical data from improper acquisition or dissemination by state agents. Part I provides background on Whalen v. Roe , the Supreme Court case that has been understood to establish the right to informational privacy. Part I also discusses the variations across the circuit courts as to what medical information is afforded protection by the right. Part II analyzes the well-established approaches adopted by the Second and Third Circuits as they present opposing interpretations of Whalen , one wholly protecting medical information and the other protecting scarcely any. Finally, Part III explains why the Supreme Court and courts that have yet to adopt a uniform approach should follow the Third Circuit and constitutionally protect all medical information from improper government acquisition or dissemination. Part III also argues for an amendment to the Privacy Act to provide individuals whose medical conditions are not afforded protection under the Constitution an alternative remedy., (Copyright by Cleveland State University.)

Background: To analyse structured and free text reports of shoulder X-ray examinations evaluating the quality of reports and potential contributions to clinical decision-making., Methods: We acquired both standard free text and structured reports of 31 patients with a painful shoulder without history of previous trauma who received X-ray exams. A template was created for the structured report based on the template ID 0000154 (Shoulder X-ray) from radreport.org using online software with clickable decision trees with concomitant generation of structured semantic reports. All reports were evaluated regarding overall quality and key features: content, information extraction and clinical relevance., Results: Two experienced orthopaedic surgeons reviewed and rated structured and free text reports of 31 patients independently. The structured reports achieved significantly higher median ratings in all key features evaluated (P < 0.001), including facilitation of information extraction (P < 0.001) and better contribution to subsequent clinical decision-making (P < 0.001). The overall quality of structured reports was significantly higher than in free text report (P < 0.001)., Conclusions: A comprehensive structured template may be a useful tool to assist in clinical decision-making and is, thus, recommended for the reporting of degenerative changes regarding X-ray examinations of the shoulder.

The current medical readiness category (CAT) status system used for military working dogs (MWDs) simply outlines the deployability of an MWD. This system, however, does not detail any other restrictions or the reason for assigning the current CAT status. The question is often raised as to whether the MWD can continue to work and perform everyday duties despite not being a CAT I. Using the Physical Profile Record system established for human providers, a system was adapted for MWDs. This system will allow Veterinary Corps Officers to give specific instructions to the handler and owning unit about the nature, progression, and details of injury or dysfunction beyond the CAT status. Furthermore, the ability to track chronic conditions and duration of illness will increase overall readiness of a kennel.

Background: The combined comorbidity score, which merges the Charlson and Elixhauser comorbidity indices, uses the ninth revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM). In October 2015, the United States adopted the 10th revision (ICD-10-CM)., Objective: The objective of this study is to examine different coding algorithms for the ICD-10-CM combined comorbidity score and compare their performance to the original ICD-9-CM score., Methods: Four ICD-10-CM coding algorithms were defined: 2 using General Equivalence Mappings (GEMs), one based on ICD-10-CA (Canadian modification) codes for Charlson and Elixhauser measures, and one including codes from all 3 algorithms. We used claims data from the Clinfomatics Data Mart to identify 2 cohorts. The ICD-10-CM cohort comprised patients who had a hospitalization between January 1, 2016 and March 1, 2016. The ICD-9-CM cohort comprised patients who had a hospitalization between January 1, 2015 and March 1, 2015. We used logistic regression models to predict 30-day hospital readmission for the original score in the ICD-9-CM cohort and for each ICD-10-CM algorithm in the ICD-10-CM cohort., Results: Distributions of each version of the score were similar. The algorithm based on ICD-10-CA codes [c-statistic, 0.646; 95% confidence interval (CI), 0.640-0.653] had the most similar discrimination for readmission to the ICD-9-CM version (c, 0.646; 95% CI, 0.639-0.653), but combining all identified ICD-10-CM codes had the highest c-statistic (c, 0.651; 95% CI, 0.644-0.657)., Conclusions: We propose an ICD-10-CM version of the combined comorbidity score that includes codes identified by ICD-10-CA and GEMs. Compared with the original score, it has similar performance in predicting readmission in a population of United States commercially insured individuals.

Importance: Hospital readmissions are being used increasingly as an indicator of quality of care. However, it remains difficult to identify potentially preventable readmissions., Objectives: To evaluate the identification of potentially preventable hospital readmissions by using a classification of readmissions based on administrative data., Design and Setting: We classified a random sample of 455 readmissions to a Dutch university hospital in 2014 using administrative data. We compared these results to a classification based on reviewing the medical records of these readmissions to evaluate the accuracy of classification by administrative data., Main Outcome Measures: Frequencies of categories of readmissions based on reviewing records versus those based on administrative data. Cohen's kappa for the agreement between both methods. The sensitivity and specificity of the identification of potentially preventable readmissions with classification by administrative data., Results: Reviewing the medical records of acute readmissions resulted in 28.5% of the records being classified as potentially preventable. With administrative data this was 44.1%. There was slight agreement between both methods: ƙ 0.08 (95% CI: 0.02-0.15, P < 0.05). The sensitivity of the classification of potentially preventable readmissions by administrative data was 63.1% and the specificity was 63.5%., Conclusions: This explorative study demonstrated differences between categorizing readmissions based on reviewing records compared to using administrative data. Therefore, this tool can only be used in practice with great caution. It is not suitable for penalizing hospitals based on their number of potentially preventable readmissions. However, hospitals might use this classification as a screening tool to identify potentially preventable readmissions more efficiently., (© The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)

Infectious and inflammatory diseases of the central nervous system are difficult to identify early. Case definitions for aseptic meningitis, encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM) are available, but rarely put to use. The VACC-Tool (Vienna Vaccine Safety Initiative Automated Case Classification-Tool) is a mobile application enabling immediate case ascertainment based on consensus criteria at the point-of-care. The VACC-Tool was validated in a quality management program in collaboration with the Robert-Koch-Institute. Results were compared to ICD-10 coding and retrospective analysis of electronic health records using the same case criteria. Of 68,921 patients attending the emergency room in 10/2010-06/2013, 11,575 were hospitalized, with 521 eligible patients (mean age: 7.6 years) entering the quality management program. Using the VACC-Tool at the point-of-care, 180/521 cases were classified successfully and 194/521 ruled out with certainty. Of the 180 confirmed cases, 116 had been missed by ICD-10 coding, 38 misclassified. By retrospective application of the same case criteria, 33 cases were missed. Encephalitis and ADEM cases were most likely missed or misclassified. The VACC-Tool enables physicians to ask the right questions at the right time, thereby classifying cases consistently and accurately, facilitating translational research. Future applications will alert physicians when additional diagnostic procedures are required.

Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve health care. In this paper, we build an integrating system for extracting medication names and symptom names from clinical notes. Then we apply nonnegative matrix factorization (NMF) and multi-view NMF to cluster clinical notes into meaningful clusters based on sample-feature matrices. Our experimental results show that multi-view NMF is a preferable method for clinical document clustering. Moreover, we find that using extracted medication/symptom names to cluster clinical documents outperforms just using words.

Background: Clinical data gathered for administrative purposes often lack sufficient information to separate the records of radiotherapy given for palliation from those given for cure. An absence, incompleteness, or inaccuracy of such information could hinder or bias the study of the utilization and outcome of radiotherapy. This study has three specific purposes: 1) develop a method to determine the therapeutic role of radiotherapy (TRR); 2) assess the accuracy of the method; 3) report the quality of the information on treatment "intent" recorded in the clinical data in Ontario, Canada. A general purpose is to use this study as a prototype to demonstrate and test a method to assess the quality of administrative data., Methods: This is a population based retrospective study. A random sample was drawn from the treatment records with "intent" assigned in treating hospitals. A decision tree is grown using treatment parameters as predictors and "intent" as outcome variable to classify the treatments into curative or palliative. The tree classifier was applied to the entire dataset, and the classification results were compared with those identified by "intent". A manual audit was conducted to assess the accuracy of the classification., Results: The following parameters predicted the TRR, from the strongest to the weakest: radiation dose per fraction, treated body-region, disease site, and time of treatment. When applied to the records of treatments given between 1990 and 2008 in Ontario, Canada, the classification rules correctly classified 96.1% of the records. The quality of the "intent" variable was as follows: 77.5% correctly classified, 3.7% misclassified, and 18.8% did not have an "intent" assigned., Conclusions: The classification rules derived in this study can be used to determine the TRR when such information is unavailable, incomplete, or inaccurate in administrative data. The study demonstrates that data mining approach can be used to effectively assess and improve the quality of large administrative datasets.

Record linkage methods commonly use a traditional latent class model to classify record pairs from different sources as true matches or non-matches. This approach was first formally described by Fellegi and Sunter and assumes that the agreement in fields is independent conditional on the latent class. Consequences of violating the conditional independence assumption include bias in parameter estimates from the model. We sought to further characterize the impact of conditional dependence on the overall misclassification rate, sensitivity, and positive predictive value in the record linkage problem when the conditional independence assumption is violated. Additionally, we evaluate various methods to account for the conditional dependence. These methods include loglinear models with appropriate interaction terms identified through the correlation residual plot as well as Gaussian random effects models. The proposed models are used to link newborn screening data obtained from a health information exchange. On the basis of simulations, loglinear models with interaction terms demonstrated the best misclassification rate, although this type of model cannot accommodate other data features such as continuous measures for agreement. Results indicate that Gaussian random effects models, which can handle additional data features, perform better than assuming conditional independence and in some situations perform as well as the loglinear model with interaction terms., (Copyright © 2014 John Wiley & Sons, Ltd.)

The ICD-10 transition will be an evolutionary process. Relying on the EHR or certified coding staff alone will not be sufficient. The EHR can facilitate easy search tools that assist the provider in selecting a diagnosis. Billing staff are an invaluable resource to help validate that coding and documentation are in sync but the burden will clearly rest on the provider. The provider will be juggling a new code structure, drilling down to new levels of complexity and ensuring their documentation supports the specificity of the new codes selected, all while managing a full patient schedule. Education for the provider will be of paramount importance as they navigate this brave new world.

Objective: Coding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters., Materials and Methods: The data were transcripts of 415 routine outpatient visits of HIV patients which had previously been coded for speech acts using the Generalized Medical Interaction Analysis System (GMIAS); 50 had also been coded for larger scale features using the Comprehensive Analysis of the Structure of Encounters System (CASES). We aggregated selected speech acts into information-giving and requesting, then trained the machine to automatically annotate using logistic regression classification. We evaluated reliability by per-speech act accuracy. We used multiple regression to predict patient reports of communication quality from post-visit surveys using the patient and provider information-giving to information-requesting ratio (briefly, information-giving ratio) and patient gender., Results: Automated coding produces moderate reliability with human coding (accuracy 71.2%, κ=0.57), with high correlation between machine and human prediction of the information-giving ratio (r=0.96). The regression significantly predicted four of five patient-reported measures of communication quality (r=0.263-0.344)., Discussion: The information-giving ratio is a useful and intuitive measure for predicting patient perception of provider-patient communication quality. These predictions can be made with automated annotation, which is a practical option for studying large collections of clinical encounters with objectivity, consistency, and low cost, providing greater opportunity for training and reflection for care providers.

Background: The purpose of this study was to assess whether or not the change in coding classification had an impact on diagnosis and comorbidity coding in hospital discharge data across Canadian provinces., Methods: This study examined eight years (fiscal years 1998 to 2005) of hospital records from the Hospital Person-Oriented Information database (HPOI) derived from the Canadian national Discharge Abstract Database. The average number of coded diagnoses per hospital visit was examined from 1998 to 2005 for provinces that switched from International Classifications of Disease 9(th) version (ICD-9-CM) to ICD-10-CA during this period. The average numbers of type 2 and 3 diagnoses were also described. The prevalence of the Charlson comorbidities and distribution of the Charlson score one year before and one year after ICD-10 implementation for each of the 9 provinces was examined. The prevalence of at least one of the seventeen Charlson comorbidities one year before and one year after ICD-10 implementation were described by hospital characteristics (teaching/non-teaching, urban/rural, volume of patients)., Results: Nine Canadian provinces switched from ICD-9-CM to ICD-I0-CA over a 6 year period starting in 2001. The average number of diagnoses coded per hospital visit for all code types over the study period was 2.58. After implementation of ICD-10-CA a decrease in the number of diagnoses coded was found in four provinces whereas the number of diagnoses coded in the other five provinces remained similar. The prevalence of at least one of the seventeen Charlson conditions remained relatively stable after ICD-10 was implemented, as did the distribution of the Charlson score. When stratified by hospital characteristics, the prevalence of at least one Charlson condition decreased after ICD-10-CA implementation, particularly for low volume hospitals., Conclusion: In conclusion, implementation of ICD-10-CA in Canadian provinces did not substantially change coding practices, but there was some coding variation in the average number of diagnoses per hospital visit across provinces.

Data obtained from the patient medical record are often a component of clinical research led by nurse investigators. The rigor of the data collection methods correlates to the reliability of the data and, ultimately, the analytical outcome of the study. Research strategies for reliable data collection from the patient medical record include the development of a precise data collection tool, the use of a coding manual, and ongoing communication with research staff., (Copyright © 2012 Elsevier Inc. All rights reserved.)

This paper describes an approach to build a Data Definition Ontology (DDO) in the context of full domain ontology integration with datasets in order to share and query clinical heterogeneous data repositories. We have adapted an existing semantic web tool (D2RQ) to implement a process that automatically generates the DDO from a database information model, thanks to reverse engineering and schema mapping approaches. This study has been performed in the context of the DebugIT European project (Detecting and Eliminating Bacteria UsinG Information Technology) that aims to control and monitor the bacterial growth via a semantic interoperability platform (IP). The evaluation of the process is based, first, on the accuracy of the produced DDO for different samples of database storage and second, by checking the congruency between the DDO and the D2RQ database mapping file.

Background: The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this context, we undertook this review to discuss: (1) the history of the International Classification of Diseases (a core information "building block" for health systems everywhere) from its introduction to the current era of ICD-11 development; (2) differences across country-specific ICD-10 clinical modifications and the challenges that these differences pose to the international comparability of morbidity data; (3) potential strategic approaches to achieving better international ICD-11 comparability., Literature Review and Discussion: A literature review and stakeholder consultation was carried out. The various ICD-10 clinical modifications (ICD-10-AM [Australia], ICD-10-CA [Canada], ICD-10-GM [Germany], ICD-10-TM [Thailand], ICD-10-CM [United States]) were compared. These ICD-10 modifications differ in their number of codes, chapters, and subcategories. Specific conditions are present in some but not all of the modifications. ICD-11, with a similar structure to ICD-10, will function in an electronic health records environment and also provide disease descriptive characteristics (eg, causal properties, functional impact, and treatment)., Conclusion: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions. One solution to this threat is to develop a meta-database including all country-specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.

Aim: To assess validity of diabetes International Classification of Disease (ICD) 9 and 10 coding algorithms from administrative data using physicians' charts as the 'gold standard' across time periods and geographic regions., Methods: From 48 urban and 16 rural general practitioners' clinics in Alberta and British Columbia, Canada, we randomly selected 50patient charts/clinic for those who visited the clinic in either 2001 or 2004. Reviewed chart data were linked with inpatient discharge abstract and physician claims administrative data. We identified patients with diabetes in the administrative databases using ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x., Results: The prevalence of diabetes was 8.1% among clinic charts. The coding algorithm of "2 physician claims within 2 years or 1 hospitalization with the relevant diabetes ICD codes" had higher validity than other 7 algorithms assessed (sensitivity 92.3%, specificity 96.9%, positive predictive value 77.2%, and negative predictive value 99.3%). After adjustment for age, sex, and comorbid conditions, sensitivity and positive predictive values were not significantly different between time periods and regions., Conclusion: Diabetes could be accurately identified in administrative data using the following case definition "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x"., (Copyright 2010 Elsevier Ireland Ltd. All rights reserved.)

International Classification of Disease coding is widely used by physicians, hospitals, health-care payers, and governments to assess the health of populations and as a means of reimbursement for medical care based on diagnosis and severity of illness. The current classification system, International Classification of Diseases, 9th ed (ICD-9), will soon be replaced by International Classification of Diseases, 10th ed (ICD-10). When the codes that relate to COPD and asthma are examined, the clinical relevance of the categories in International Classification of Disease coding must be questioned. In the future, a more simplified terminology that is consistent with clinical usage could improve accuracy and ease of coding. At present, however, clinicians should become familiar with the present ICD-9 and future ICD-10 codes so that their descriptions of illnesses in the medical records more accurately reflect current coding terminology.

Introduction: Because of the relatively high expense of collecting primary data and limited resources, electronically available, population-based hospital discharge data have been increasingly used for disease surveillance by public health researchers. The objective of this study was to compare the New York State Congenital Malformations Registry (CMR) data, which relies on hospital reports, with the hospital discharge files to identify cases in the CMR that were missed in the hospital discharge data files. The ultimate goal was to evaluate whether hospital discharge data can serve as the sole source of case ascertainment for a population-based birth defects surveillance program., Methods: CMR cases that were born to the New York State residents for the years 2000 to 2005 were selected and matched to the hospital discharge files from the New York Statewide Planning and Research Cooperative System (SPARCS) for the same birth year period. Since the SPARCS database does not contain patient's name, extensive database matching and manual review by staff members were performed using identifying variables such as the hospital's permanent facility identifier, child's date of birth and medical record number, and mother's medical record number and residential address., Results: Out of 66 757 CMR cases selected for the study period, 62 118 cases (93.1%) were matched to SPARCS hospital discharge records with International Classification of Diseases, Ninth Revision (ICD-9) codes that were reportable to the CMR, 3 444 cases (5.2%) were matched to SPARCS records with ICD-9 codes that were not reportable to the CMR, and 1 195 cases (1.8%) were not matched. The percentage of cases with multiple congenital malformations was significantly higher (21.3%) for the matched cases that had reportable ICD-9 codes in SPARCS, compared with that for matched CMR cases that had no reportable ICD-9 codes in SPARCS (10.2%)., Conclusion: The study found that 93% of CMR infants selected for the study were matched to hospital discharge records with at least one ICD-9 code that was reportable to the CMR; 87 percent had reportable ICD codes in SPARCS that were exactly matched to those in the CMR, that is, all the birth defect codes in SPARCS were matched to those in the CMR. Thus, about 7 percent of CMR children with birth defects would have been missed if only hospital discharge files were used to ascertain the birth defect cases, indicating that there are limitations to using hospital discharge files as the sole source of case ascertainment for population-based birth defects surveillance programs.

Objective: To develop a tool to allow Australian hospitals to monitor the range of hospital-acquired diagnoses coded in routine data in support of quality improvement efforts., Design and Setting: Secondary analysis of abstracted inpatient records for all episodes in acute care hospitals in Victoria for the financial year 2005-06 (n=2.032 million) to develop a classification system for hospital-acquired diagnoses; each record contains up to 40 diagnosis fields coded with the ICD-10-AM (International Classification of Diseases, 10th revision, Australian modification)., Main Outcome Measure: The Classification of Hospital Acquired Diagnoses (CHADx) was developed by: analysing codes with a "complications" flag to identify high-volume code groups; assessing their salience through an iterative review by health information managers, patient safety researchers and clinicians; and developing principles to reduce double counting arising from coding standards., Results: The dataset included 126,940 inpatient episodes with any hospital-acquired diagnosis (complication rate, 6.25%). Records had a mean of three flagged diagnoses; including unflagged obstetric and neonatal codes, 514,371 diagnoses were available for analysis. Of these, 2.9% (14,898) were removed as comorbidities rather than complications, and another 118,640 were removed as redundant codes, leaving 380,833 diagnoses for grouping into CHADx classes. We used 4345 unique codes to characterise hospital-acquired conditions; in the final CHADx these were grouped into 144 detailed subclasses and 17 "roll-up" groups., Conclusions: Monitoring quality improvement requires timely hospital-onset data, regardless of causation or "preventability" of each complication. The CHADx uses routinely abstracted hospital diagnosis and condition-onset information about in-hospital complications. Use of this classification will allow hospitals to track monthly performance for any of the CHADx indicators, or to evaluate specific quality improvement projects.

In the clinical laboratory to allow an effective testing process, post-analytical activity can have two goals in trying to improve patient safety: result interpretation and communication of critical values. Both are important issues, and their success requires a cooperative effort. Misinterpretation of laboratory test results or ineffectiveness in their notification can lead to diagnostic errors or errors in identifying patient critical conditions. With the awareness that the incorrect interpretation of tests and the breakdown in the communication of critical values are preventable errors, laboratorians should make every effort to prevent the types of errors that potentially harm patients. In order to improve the reliability of laboratories, we attempt to explain how interpretative reporting and automated notification of critical values can be used to reduce errors. Clinical laboratories can therefore work to improve clinical effectiveness, without forgetting that everything should be designed to provide the best outcomes for patients.

Standardisation of the classification of breast imaging reports will improve communication between the referrer and the radiologist and avoid ambiguity, which may otherwise lead to mismanagement of patients. Following wide consultation, the Royal College of Radiologists Breast Group has produced a scoring system for the classification of breast imaging. This will facilitate audit and the development of nationally agreed standards for the investigation of women with breast disease. This five-point system is as follows: 1, normal; 2, benign findings; 3, indeterminate/probably benign findings; 4, findings suspicious of malignancy; 5, findings highly suspicious of malignancy. It is recommended that this be used in the reporting of all breast imaging examinations in the UK.

Hospital Information Systems (HIS) handle a large number of different types of documents. Exchange and analysis of data from different HIS is facilitated by the use of standardized codes to identify document types. HL7's Clinical Document Architecture (CDA) uses LOINC (logical observation identifiers names and Codes) codes for clinical documents. The authors assessed the coverage of LOINC codes for document types in a German HIS. The authors analyzed document types that occurred more than 10 times in approximately 1.3 million documents in a commercial HIS at a major German University Hospital. Document types were mapped manually to LOINC using the Regenstrief LOINC Mapping Assistant (RELMA). Each document type was coded by two physicians. In case of discrepancies a third expert was consulted to reach consensus. For 76 of 86 document categories a LOINC code was identified, but for 38 of these categories, the LOINC code was not specific as deemed necessary. More than 93% of our local HIS documents had local document types that could be assigned a LOINC code.

Background and Objective: Hospital-acquired catheter-associated urinary tract infection (CAUTI) is one of the first 6 conditions Medicare is targeting to reduce payment associated with hospital-acquired conditions under Congressional mandate. This study was to determine the positive predictive value (PPV) and sensitivity in identifying patients in Medicare claims who had urinary catheterization and who had hospital-acquired CAUTIs., Research Design: CAUTIs identified by ICD-9-CM codes in Medicare claims were compared with those revealed by medical record abstraction in random samples of Medicare discharges in 2005 to 2006. Hospital discharge abstracts (2005) from the states of New York and California were used to estimate the potential impact of a present-on-admission (POA) indicator on PPV., Results: ICD-9-CM procedure codes for urinary catheterization appeared in only 1.4% of Medicare claims for patients who had urinary catheters. As a proxy, claims with major surgery had a PPV of 75% and sensitivity of 48%, and claims with any surgical procedure had a PPV of 53% and sensitivity of 79% in identifying urinary catheterization. The PPV and sensitivity for identifying hospital-acquired CAUTIs varied, with the PPV at 30% and sensitivity at 65% in claims with major surgery. About 80% of the secondary diagnosis codes indicating UTIs were flagged as POA, suggesting that the addition of POA indicators in Medicare claims would increase PPV up to 86% and sensitivity up to 79% in identifying hospital-acquired CAUTIs., Conclusions: The validity in identifying urinary catheter use and CAUTIs from Medicare claims is limited, but will be increased substantially upon addition of a POA indicator.

Effective October 1, 2007, the Centers for Medicare and Medicaid Services has changed its methodology for determining the diagnosis-related group for hospitalized patients. In an effort to more accurately reflect severity of illness, the 538 diagnosis-related groups have been converted to 745 new Medicare severity diagnosis-related groups. In addition, selected hospital-acquired complications not identified as present on admission will no longer be reimbursed. The changes will have profound effects on reimbursement for hospitalizations. To minimize financial losses under the new rules, hospitals and physicians will have to devote significant resources and attention to improved documentation. This article will discuss the new payment system, the physician's role in ensuring that all clinically important diagnoses are captured by coding specialists, and strategies that can be employed to respond proactively to the challenge.

Objectives: The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification., Design: For each mention of each medical problem, both approaches determine whether the problem, as asserted by the context of that mention, is present, absent, or uncertain in the patient, or associated with someone other than the patient. The authors use these two systems to (1) extend negation and uncertainty extraction to recognition of alter-association assertions, (2) determine the contribution of lexical and syntactic context to assertion classification, and (3) test if a machine learning approach to assertion classification can be as generally applicable and useful as its rule-based counterparts., Measurements: The authors evaluated assertion classification approaches with precision, recall, and F-measure., Results: The ENegEx algorithm is a general algorithm that can be directly applied to new corpora. Despite being based on machine learning, StAC can also be applied out-of-the-box to new corpora and achieve similar generality., Conclusion: The StAC models that are developed on discharge summaries can be successfully applied to radiology reports. These models benefit the most from words found in the +/- 4 word window of the target and can outperform ENegEx.

Introduction: Osteonecrosis (ON) is a rare disease associated with alcohol and glucocorticoid use. Identifying additional risk factors is difficult as the number of cases at any single center is small. We investigated whether data available in large health care databases can be used to identify incident ON cases., Methods: Using data from the Boston Veterans Affairs Healthcare system, we identified potential cases of ON. These records, including available radiographs and reports, were reviewed. Using published criteria, we evaluated whether the subjects had confirmed ON (radiographs/reports met criteria), incident ON (onset of symptoms within 6 months of first code), or prevalent ON (onset more than 6 months prior to first code or onset could not be determined). We tested different definitions for incident ON using information derived from administrative data. These were compared to the 'gold standard' (record review) and positive predictive values (PPVs) were derived. Since PPVs for incident cases were low, we found the number of incident cases expected for every 1,000 potential cases identified, using the definitions as an initial screening tool to reduce the number of medical records that required examination., Results: We identified 87 potential cases. No case of jaw ON was identified. Only 15 (17%) incident cases of ON were identified. PPVs never exceed 50% for incident ON. However, if we used the definition '(at least 1 inpatient ON code) and (no prior codes for osteoarthritis)' as an initial screen, then for every 1,000 records, we would need to review only 150 to find 69 incident cases., Conclusions: Though the precise PPVs we found may not be generalizable to other databases, we believe that administrative data alone should not be used to identify incident cases of ON without confirming the diagnosis through a review of medical records. By applying the above definition, the number of records requiring review can be markedly reduced. This method can be used to find cases for valid case-control studies of risk factors for ON.

Objectives: Many of the 2.5 million Americans assaulted annually by intimate partners seek medical care. This project evaluated diagnostic codes indicative of intimate partner violence (IPV) in Oregon hospital and emergency department (ED) records to determine predictive value positive (PVP), sensitivity, and usefulness in routine surveillance. Statewide incidence of care for IPV was calculated and victims and episodes characterized., Methods: The study was a review of medical records assigned > or = 1 diagnostic codes thought predictive of IPV. Sensitivity was estimated by comparing the number of confirmed victims identified with the number predicted by statewide telephone survey. Patients were aged > or = 12 years, treated in any of 58 EDs or hospitals in Oregon during 2000, and discharged with one of three primary or 12 provisional codes suggestive of IPV. Outcome measures were number of victims detected, PPV and sensitivity of codes for detection of IPV, and description of victims., Results: Of 58 hospitals, 52 (90%) provided records. Case finding using primary codes identified 639 victims, 23% of all estimated female victims seen in EDs or hospitalized statewide. PVP was 94% (639/677). Provisional codes increased sensitivity (51%) but reduced PVP (50%). Highest incidence occurred in women aged 20-39 years, and those who were black. Hospitalizations were highest among women aged > or = 50 years, black people, or those with comorbid illness., Conclusions: Three diagnostic codes used for case finding detect approximately one-quarter of ED- and hospital-treated victims, complement surveys, and facilitate description of injured victims.

This proposed rule would modify two of the medical data code set standards adopted in the Transactions and Code Sets final rule published in the Federal Register. It would also implement certain provisions of the Administrative Simplification subtitle of the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Specifically, the proposed rule would modify the standard code sets for coding diagnoses and inpatient hospital procedures by concurrently adopting the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for diagnosis coding, and the International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) for inpatient hospital procedure coding. These new codes would replace the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9- CM) Volumes 1 and 2, and the International Classification of Diseases, Ninth Revision, Clinical Modification (CM) Volume 3 for diagnosis and procedure codes, respectively.

Objective: The goal of this study was to assess the validity of the International Classification of Disease, 10th Version (ICD-10) administrative hospital discharge data and to determine whether there were improvements in the validity of coding for clinical conditions compared with ICD-9 Clinical Modification (ICD-9-CM) data., Methods: We reviewed 4,008 randomly selected charts for patients admitted from January 1 to June 30, 2003 at four teaching hospitals in Alberta, Canada to determine the presence or absence of 32 clinical conditions and to assess the agreement between ICD-10 data and chart data. We then re-coded the same charts using ICD-9-CM and determined the agreement between the ICD-9-CM data and chart data for recording those same conditions. The accuracy of ICD-10 data relative to chart data was compared with the accuracy of ICD-9-CM data relative to chart data., Results: Sensitivity values ranged from 9.3 to 83.1 percent for ICD-9-CM and from 12.7 to 80.8 percent for ICD-10 data. Positive predictive values ranged from 23.1 to 100 percent for ICD-9-CM and from 32.0 to 100 percent for ICD-10 data. Specificity and negative predictive values were consistently high for both ICD-9-CM and ICD-10 databases. Of the 32 conditions assessed, ICD-10 data had significantly higher sensitivity for one condition and lower sensitivity for seven conditions relative to ICD-9-CM data. The two databases had similar sensitivity values for the remaining 24 conditions., Conclusions: The validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions was generally similar though validity differed between coding versions for some conditions. The implementation of ICD-10 coding has not significantly improved the quality of administrative data relative to ICD-9-CM. Future assessments like this one are needed because the validity of ICD-10 data may get better as coders gain experience with the new coding system.

Background: We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance., Methods: A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995-2001 based on the ICD-9-CM coding algorithm, and for 2002-2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995-2005. The corresponding administrative data records were coded in ICD-9-CM for 1995-2001 and in ICD-10 for 2002-2005. The resulting datasets were then evaluated for their ability to predict death at one year., Results: The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit., Conclusion: The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.

Little is known about factors related to rehospitalizations that occur soon after discharge in patients with congestive heart failure (CHF). The aim of this study was to determine if there are specific characteristics common to CHF patients readmitted within 30 days. Study methods included retrospective chart review. Patients included all those hospitalized and readmitted to a large community teaching hospital for CHF exacerbation within 30 days between January 7, 2005 and June 30, 2006. A total of 58 patients were responsible for 79 readmissions. Half of all patients readmitted for CHF exacerbation were incorrectly labeled, raising doubt about reliance on administrative data alone to determine overall quality performance. Forty-five percent of all readmitted patients had underlying chronic renal insufficiency/failure (CRI/CRF) compared with 26% of CHF patients who were not readmitted within 30 days. Therefore, specifically targeting CHF patients with CRI/CRF could lead to major improvement of early readmission rates.

The influence of organisational factors on the quality of hospital coding using the International Statistical Classification of Diseases and Health Related Problems, 10th Revision, Australian Modification (ICD-10-AM) was investigated using a mixed quantitative-qualitative approach. The organisational variables studied were: hospital specialty; geographical locality; structural characteristics of the coding unit; education, training and resource supports for Clinical Coders; and quality control mechanisms. Baseline data on the hospitals' coding quality, measured by the Performance Indicators for Coding Quality tool, were used as an independent index measure. No differences were found in error rates between rural and metropolitan hospitals, or general and specialist hospitals. Clinical Coder allocation to "general" rather than "specialist" unit coding resulted in fewer errors. Coding Managers reported that coding quality can be improved by: Coders engaging in a variety of role behaviours; improved Coder career opportunities; higher staffing levels; reduced throughput; fewer time constraints on coding outputs and associated work; and increased Coder interactions with medical staff.

Objective: To determine relationship between length-based resuscitation tape (LBT)-based color-coded zones and actual weight-based color-coded zones., Methods: Data were retrospectively abstracted from 839 patients in Rochester, Minnesota, at birth, 4 to 6, and 10 to 12 years. Height was plotted on LBT to determine estimated weight and corresponding color zone. Patient's weight-based color zone was obtained by plotting measured weight on LBT. Degrees of discrepancy between length-based and actual weight-based color zones were assessed., Results: Total of 544, 520, and 143 subjects were analyzed at birth, 4 to 6, and 10 to 12 years, respectively, with a subset of 103 subjects measured longitudinally at more than 1 age strata. Among infants, all LBT color zones were the same as actual weight-based color zones. In children aged 4 to 6 years, 70% (n = 361) of LBT-estimated color zones were the same as actual weight-based color zones; LBT underestimated 19% (n = 99) by 1 color zone, 0.5% (n = 3) were underestimated by 2 color zones, 0.5% (n = 3) exceeded weight limit on LBT, and 10% (n = 54) were overestimated by 1 color zone. In adolescents aged 10 to 12 years, 40.6% (n = 58) of LBT-estimated color zones were the same as actual weight-based color zones; LBT underestimated 3.5% (n = 5) by 1 color zone, 44.1% (n = 63) exceeded weight limit on LBT, 11.2% (n = 16) were overestimated by 1 color zone, and 0.6% (n = 1) were overestimated by 2 color zones., Conclusions: Overall, LBT reasonably estimates appropriate color zones for drug dosing. However, LBT tends to underestimate color zones among younger obese children and adolescents. Potential implications of the rising trend of overweight children on resuscitation practice and drug administration must be considered.

Cancer staging provides a basis for planning clinical management, but also allows for meaningful analysis of cancer outcomes and evaluation of cancer care services. Despite this, stage data in cancer registries is often incomplete, inaccurate, or simply not collected. This article describes a prototype software system (Cancer Stage Interpretation System, CSIS) that automatically extracts cancer staging information from medical reports. The system uses text classification techniques to train support vector machines (SVMs) to extract elements of stage listed in cancer staging guidelines. When processing new reports, CSIS identifies sentences relevant to the staging decision, and subsequently assigns the most likely stage. The system was developed using a database of staging data and pathology reports for 710 lung cancer patients, then validated in an independent set of 179 patients against pathologic stage assigned by two independent pathologists. CSIS achieved overall accuracy of 74% for tumor (T) staging and 87% for node (N) staging, and errors were observed to mirror disagreements between human experts.

Background: The first step of handling health promotion (HP) in Diagnosis Related Groups (DRGs) is a systematic documentation and registration of the activities in the medical records. So far the possibility and tradition for systematic registration of clinical HP activities in the medical records and in patient administrative systems have been sparse. Therefore, the activities are mostly invisible in the registers of hospital services as well as in budgets and balances.A simple model has been described to structure the registration of the HP procedures performed by the clinical staff. The model consists of two parts; first part includes motivational counselling (7 codes) and the second part comprehends intervention, rehabilitation and after treatment (8 codes).The objective was to evaluate in an international study the usefulness, applicability and sufficiency of a simple model for the systematic registration of clinical HP procedures in day life., Methods: The multi centre project was carried out in 19 departments/hospitals in 6 countries in a clinical setup. The study consisted of three parts in accordance with the objectives.A: Individual test. 20 consecutive medical records from each participating department/hospital were coded by the (coding) specialists at local department/hospital, exclusively (n = 5,529 of 5,700 possible tests in total).B: Common test. 14 standardized medical records were coded by all the specialists from 17 departments/hospitals, who returned 3,046 of 3,570 tests.C: Specialist evaluation. The specialists from the 19 departments/hospitals evaluated if the codes were useful, applicable and sufficient for the registration in their own department/hospital (239 of 285)., Results: A: In 97 to 100% of the local patient pathways the specialists were able to evaluate if there was documentation of HP activities in the medical record to be coded.B: Inter rater reliability on the use of the codes were 93% (57 to 100%) and 71% (31 to 100%), respectively.C: The majority of the study participants found the codes to be useful (71%), applicable (92%) and sufficient (92%)., Conclusion: Systematic registration of HP activities is relevant in clinical day life and the suggested codes proved to be applicable for international use. HP is an essential part of the clinical pathway or the value chain. This model promises to improve the documentation and thereby facilitate analysis of records for evidence based medicine as well as cost and policy analyses.