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PubMed2001

Public release of performance data and quality improvement: internal responses to external data by US health care providers.

Health policy in many countries emphasises the public release of comparative data on clinical performance as one way of improving the quality of health care. Evidence to date is that it is health care providers (hospitals and the staff within them) that are most likely to respond to such data, yet little is known about how health care providers view and use these data. Case studies of six US hospitals were studied (two academic medical centres, two private not-for-profit medical centres, a group model health maintenance organisation hospital, and an inner city public provider "safety net" hospital) using semi-structured interviews followed by a broad thematic analysis located within an interpretive paradigm. Within these settings, 35 interviews were held with 31 individuals (chief executive officer, chief of staff, chief of cardiology, senior nurse, senior quality managers, and front line staff). The results showed that key stakeholders in these providers were often (but not always) antipathetic towards publicly released comparative data. Such data were seen as lacking in legitimacy and their meanings were disputed. Nonetheless, the public nature of these data did lead to some actions in response, more so when the data showed that local performance was poor. There was little integration between internal and external data systems. These findings suggest that the public release of comparative data may help to ensure that greater attention is paid to the quality agenda within health care providers, but greater efforts are needed both to develop internal systems of quality improvement and to integrate these more effectively with external data systems.

Attitude of Health Personnel↗

PubMed2004

Improving the quality of data in your database: lessons from a cardiovascular center.

BACKGROUND: Creating and having a database should not be an end goal but rather a source of valid data and a means for generating information by which to assess process, performance, and outcome quality. The Cardiovascular Center at Shands Jacksonville (Florida) made measurable improvements in the quality of data in national registries and internally available software tools for collection of patient care data. METHODS: The process of data flow was mapped from source to report submission to identify input timing and process gaps, data sources, and responsible individuals. Cycles of change in data collection and entry were developed and the improvements were tracked. RESULTS: Data accuracy was improved by involving all caregivers in datasheet completion and assisting them with data-field definitions. Using hospital electronic databases decreased the need for manual retrospective review of medical records for datasheet completion. The number of fields with missing values decreased by 83.6%, and the number of missing values decreased from 31.2% to 1.9%. Data accuracy rose dramatically by realtime data entry at point of care. DISCUSSION: Key components to ensuring data quality for process and outcome improvement are (1) education of the caregiver team, (2) process supervision by a database manager, (3) commitment and explicit support from leadership,(4) increased and improved use of electronic data sources, and (5) data entry at point of care.

Cardiology↗

PubMed1995

Improving the quality of cancer registration data.

Cancer registration is an essential element of any cancer control strategy. Data quality is, however, of paramount importance. This paper sets out some of the ways in which the quality of cancer registration data might be improved. In particular, the potential contribution of clinicians and pathologists is highlighted.

Data Collection↗

PubMed2004

Di-p-bromophenyl ether, a redetermined crystal structure derived from low-quality diffraction data.

We show that the lack of good quality data, normally essential to successful crystal structure analysis, can in part be compensated for by measuring data from several crystals and merging the resulting data sets. The crystal structure of the flame retardant di-p-bromophenyl ether, C12H8Br2O, a twofold axially symmetric molecule, has been redetermined and refined from such a merged multi-crystal diffraction data set to an acceptable conventional R factor (R1 = 0.06), a result which could not have been obtained from any one of our single-crystal diffraction data sets used alone in the normal manner.

Journal Article↗

PubMedDate not supplied

Reporting quality improvement data by key functions: one step closer to continuous quality improvement.

Medical care systems must demonstrate that they provide cost-effective, quality care if they are to remain viable in the wake of healthcare reform. A basic premise of continuous quality improvement is that an organization can measure and improve the performance of key functions (a group of goal-directed processes). Accordingly, the Joint Commission on Accreditation of Healthcare Organizations has been restructuring its accreditation manual, moving from department-specific standards to functional standards. In early 1992, our hospital began reporting quality improvement data by key functions rather than by service. As a result of this change, departments communicated and worked together better and more interdisciplinary efforts occurred, resulting in some dramatic improvements in the quality of care.

Data Collection↗

PubMed2003

Training in data definitions improves quality of intensive care data.

BACKGROUND: Our aim was to assess the contribution of training in data definitions and data extraction guidelines to improving quality of data for use in intensive care scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II in the Dutch National Intensive Care Evaluation (NICE) registry. METHODS: Before and after attending a central training programme, a training group of 31 intensive care physicians from Dutch hospitals who were newly participating in the NICE registry extracted data from three sample patient records. The 5-hour training programme provided participants with guidelines for data extraction and strict data definitions. A control group of 10 intensive care physicians, who were trained according the to train-the-trainer principle at least 6 months before the study, extracted the data twice, without specific training in between. RESULTS: In the training group the mean percentage of accurate data increased significantly after training for all NICE variables (+7%, 95% confidence interval 5%-10%), for APACHE II variables (+6%, 95% confidence interval 4%-9%) and for SAPS II variables (+4%, 95% confidence interval 1%-6%). The percentage data error due to nonadherence to data definitions decreased by 3.5% after training. Deviations from 'gold standard' SAPS II scores and predicted mortalities decreased significantly after training. Data accuracy in the control group did not change between the two data extractions and was equal to post-training data accuracy in the training group. CONCLUSION: Training in data definitions and data extraction guidelines is an effective way to improve quality of intensive care scoring data.

APACHE↗

PubMed2000

An assessment of DHS maternal mortality indicators.

This study presents an assessment of the quality of data relating to maternal mortality collected in 14 Demographic and Health Surveys (DHS) for 13 countries that included a complete sibling history. Four aspects of data quality are considered: completeness of the data for reported events, evidence of omission in the reporting of events, plausibility of the pattern of sibling deaths, and sampling errors of the maternal mortality estimates. Although the data relating to reported events are complete for most variables, comparisons of sibling-history-based estimates of adult mortality for both males and females with other independent estimates suggest that sibling estimates are more likely to be underestimates than overestimates. The downward bias is probably greater for female mortality than for male mortality. The sampling errors associated with maternal mortality ratios are substantially larger than those associated with other frequently used DHS indicators. This lack of precision precludes the use of these data for trend analysis and has led to the recommendation that this DHS module not be used more than once every ten years in the same country.

Adolescent↗

PubMed1997

The use of a systemic therapy checklist improves the quality of data acquisition and recording in multicentre trials. A study of the EORTC Soft Tissue and Bone Sarcoma Group.

The aim of this study was to verify whether the introduction of a systemic therapy checklist in the performance of multinational multicentre studies improves the quality of data acquisition and recording. We retrospectively analysed the results obtained through the use of this checklist in a study of the EORTC Soft Tissue and Bone Sarcoma Group. During the clinical trial, data were recorded in the hospital record with optional use of a predesigned EORTC Systemic Therapy Checklist. After completion of the study, 11 centres were monitored for the use of this checklist. Monitors were highly experienced medical oncologists. Items checked included all aspects of patient eligibility, drug administration, biochemical and haematological values, variables related to toxicities of treatment and response parameters. Data of 183 cycles given to 51 patients were checked. A total of 8983 items were checked. 91% of the data was reported correctly, 1% was missing and 6% was reported on the case record from (CRF) but could not be retrieved in the hospital record file. Compared with data obtained before the introduction of the checklist (68% correct, 4% incorrect, 0.1% missing and 28% on CRF but not in hospital files), these results show marked improvement generally. In centres where no Systemic Therapy Checklist was used, 85.9% of data were correct 2.8% incorrect, 0.7% missing and 10.6% only on CRF, which compares unfavourably with those where the Systemic Checklist was completely used (97.7% correct, 0.7% incorrect, 1% missing, 0.6% only on CRF). In addition the time required for data checking largely decreased by the use of the checklist-without this, a median of 3.5 cycles could be checked per hour, whilst if the checklist was used, this number increased to 6.5 cycles per hour. The use of a Systemic Therapy Checklist as an integral part of the hospital file for data recording in multicentre multinational trials is highly recommended and leads to a major improvement in data quality.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2000

The art and science of chart review.

BACKGROUND: Explicit chart review was an integral part of an ongoing national cooperative project, "Using Achievable Benchmarks of Care to Improve Quality of Care for Outpatients with Depression," conducted by a large managed care organization (MCO) and an academic medical center. Many investigators overlook the complexities involved in obtaining high-quality data. Given a scarcity of advice in the quality improvement (QI) literature on how to conduct chart review, the process of chart review was examined and specific techniques for improving data quality were proposed. METHODS: The abstraction tool was developed and tested in a prepilot phase; perhaps the greatest problem detected was abstractor assumption and interpretation. The need for a clear distinction between symptoms of depression or anxiety and physician diagnosis of major depression or anxiety disorder also became apparent. In designing the variables for the chart review module, four key aspects were considered: classification, format, definition, and presentation. For example, issues in format include use of free-text versus numeric variables, categoric variables, and medication variables (which can be especially challenging for abstraction projects). Quantitative measures of reliability and validity were used to improve and maintain the quality of chart review data. Measuring reliability and validity offers assistance with development of the chart review tool, continuous maintenance of data quality throughout the production phase of chart review, and final documentation of data quality. For projects that require ongoing abstraction of large numbers of clinical records, data quality may be monitored with control charts and the principles of statistical process control. RESULTS: The chart review module, which contained 140 variables, was built using MedQuest software, a suite of tools designed for customized data collection. The overall interrater reliability increased from 80% in the prepilot phase to greater than 96% in the final phase (which included three abstractors and 465 unique charts). The mean time per chart was calculated for each abstractor, and the maximum value was 13.7 +/- 13 minutes. CONCLUSIONS: In general, chart review is more difficult than it appears on the surface. It is also project specific, making a "cookbook" approach difficult. Many factors, such as imprecisely worded research questions, vague specification of variables, poorly designed abstraction tools, inappropriate interpretation by abstractors, and poor or missing recording of data in the chart, may compromise data quality.

Data Collection↗

PubMedDate not supplied

The role of field auditing in environmental quality assurance management.

Environmental data quality improvement continues to focus on analytical laboratoryperformance with little, if any, attention given to improving the performance of field consultants responsible for sample collection. Many environmental professionals often assume that the primary opportunity for data error lies within the activities conducted by the laboratory. Experience in the evaluation of environmental data and project-wide quality assurance programs indicates that an often-ignored factor affecting environmental data quality is the manner in which a sample is acquired and handled in the field. If a sample is not properly collected, preserved, stored, and transported in the field, even the best laboratory practices and analytical methods cannot deliver accurate and reliable data (i.e., bad data in equals bad data out). Poor quality environmental data may result in inappropriate decisions regarding site characterization and remedial action. Field auditing is becoming an often-employed technique for examining the performance of the environmental sampling field team and how their performance may affect data quality. The field audits typically focus on: (1) verifying that field consultants adhere to project control documents (e.g., Work Plans and Standard Operating Procedures [SOPs]) during field operations; (2) providing third-party independent assurance that field procedures, quality assurance/ quality control (QA/QC)protocol, and field documentation are sufficient to produce data of satisfactory quality; (3) providing a defense in the event that field procedures are called into question; and (4) identifying ways to reduce sampling costs. Field audits are typically most effective when performed on a surprise basis; that is, the sampling contractor may be aware that a field audit will be conducted during some phase of sampling activities but is not informed of the specific day(s) that the audit will be conducted. The audit also should be conducted early on in the sampling program such that deficiencies noted during the audit can be addressed before the majority of field activities have been completed. A second audit should be performed as a follow-up to confirm that the recommended changes have been implemented. A field auditor is assigned to the project by matching, as closely as possible, the auditor's experience with the type of field activities being conducted. The auditor uses a project-specific field audit checklist developed from key information contained in project control documents. Completion of the extensive audit checklist during the audit focuses the auditor on evaluating each aspect of field activities being performed. Rather than examine field team performance after sampling, a field auditor can do so while the samples are being collected and can apply real-time corrective action as appropriate. As a result of field audits, responsible parties often observe vast improvements in their consultant's field procedures and, consequently, receive more reliable and representative field data at a lower cost. The cost savings and improved data quality that result from properly completed field audits make the field auditing process both cost-effective and functional.

Environmental Monitoring↗

PubMed1985

Who 'controls' quality control data analysis?

A common quality control tool is peer group comparison of data from commercial controls. While its real-time effectiveness is limited, inappropriate statistical management of the data can cause an individual lab's performance to be misrepresented. Here are two examples where vendor-directed data analysis contained flagrant errors. The finding that vendors use inappropriate algorithms to compare accuracy and precision of peer performance suggests a need, the author believes, to set rigorous standards of reporting for the protection of participating laboratories.

California↗

PubMed1999

Telephone interviews vs. workstation sessions for acquiring quality of life data.

Patient quality of life data can be acquired in a variety of ways, including over the telephone and through computerized questionnaires. However, if the method of collection produces different results, medical decisions regarding appropriate and cost-effective care may be influenced by collection method. We conducted an experiment where subjects had two quality of life measures, the time trade-off and rating scale utilities, assessed both in telephone interivews and via computer touchscreens. The order of telephone and touchscreen was randomized. We found that rating scale utilities were similar whether obtained via the telephone or via touchscreen regardless of which was done first. However, patients who had their time trade-off utilities assessed over the telephone first did not provide as consistent responses as those elicited first via touchscreen (p = 0.01). Caution is suggested when considering eliciting time trade-off over the telephone with subjects who have not had time trade-off elicited previously.

Analysis of Variance↗

PubMed1995

Interpreting quality improvement data with time-series analyses.

In quality improvement efforts, the data are frequently a series of measurements taken over time. A collection of statistical methods, commonly referred to as time-series analysis, provides a simple and understandable method for interpreting this longitudinal data. In this article, we present a time-series analysis of data on the quality of prenatal care at a mid-sized public hospital. We will demonstrate some simple tests that alert us to the potential value of using more sophisticated tests of association such as regression. Using regression, we show how to confirm a visual impression of an improvement. The analytical approach we present here is useful with many types of process or outcome data from health care quality improvement efforts.

Data Collection↗

PubMed2006

Epidemiological studies in the information and genomics era: experience of the Clinical Genome of Cancer Project in São Paulo, Brazil.

Genomics is expanding the horizons of epidemiology, providing a new dimension for classical epidemiological studies and inspiring the development of large-scale multicenter studies with the statistical power necessary for the assessment of gene-gene and gene-environment interactions in cancer etiology and prognosis. This paper describes the methodology of the Clinical Genome of Cancer Project in São Paulo, Brazil (CGCP), which includes patients with nine types of tumors and controls. Three major epidemiological designs were used to reach specific objectives: cross-sectional studies to examine gene expression, case-control studies to evaluate etiological factors, and follow-up studies to analyze genetic profiles in prognosis. The clinical groups included patients' data in the electronic database through the Internet. Two approaches were used for data quality control: continuous data evaluation and data entry consistency. A total of 1749 cases and 1509 controls were entered into the CGCP database from the first trimester of 2002 to the end of 2004. Continuous evaluation showed that, for all tumors taken together, only 0.5% of the general form fields still included potential inconsistencies by the end of 2004. Regarding data entry consistency, the highest percentage of errors (11.8%) was observed for the follow-up form, followed by 6.7% for the clinical form, 4.0% for the general form, and only 1.1% for the pathology form. Good data quality is required for their transformation into useful information for clinical application and for preventive measures. The use of the Internet for communication among researchers and for data entry is perhaps the most innovative feature of the CGCP. The monitoring of patients' data guaranteed their quality.

Adult↗

PubMedDate not supplied

Does feedback improve the quality of computerized medical records in primary care?

OBJECTIVE: The MediPlus database collects anonymized information from generalpractice computer systems in the United Kingdom, for research purposes. Data quality markers are collated and fed back to the participating general practitioners. The authors examined whether this feedback had a significant effect on data quality. METHODS: The data quality markers used since 1992 were examined. The authors determined whether the feedback of "useful" data quality markers led to a statistically significant improvement in these markers. Environmental influences on data quality from outside the scheme were controlled for by examination of the data quality scores of new entrants. RESULTS: Three quality markers improved significantly over the period of the study. These were the use of highly specific "lower-level" Read Codes (p=0.004) and the linkage of repeat prescriptions (p=0.03) and acute prescriptions (p=0.04) to diagnosis. Clinicians who fall below the target level for linkage of repeat prescriptions to diagnosis receive more detailed feedback; the effect of this was also statistically significant (p<0.01.) CONCLUSIONS: The feedback of four of the ten markers had a significant effect on data quality. The effect of more detailed feedback appears to have had a greater effect. The lessons learned from this approach may help improve the quality of electronic medical records in the United Kingdom and elsewhere.

Humans↗

PubMed2006

[Data collection for quality assurance in neonatology: how do physicians compare to documentation specialists?].

BACKGROUND: The quality of data collected for the German nationwide quality assurance program in neonatology is currently unknown. The aim of this study was to compare the quality of data collected by resident physicians with the quality of similar data collected by a dedicated research nurse. METHODS: Data for the German national quality assurance program in neonatology, derived from a cohort of 128 premature newborns with a birth weight <1500 g and/or a gestational age of <30 weeks born in the year 2003, were collected by residents taking care on these patients, and separately by a dedicated research nurse for the European Neonatal Network (EuroNeoNet). The data set collected for both networks included 44 common data items. The two data sets were compared, and any disagreement was double-checked using the chart of the baby to clarify which of the data entries was wrong. Furthermore, as data items are not equally important, a weighted analysis of all mistakes was performed. RESULTS: We found wrong data in 108/128 (84 %) of the data sets collected by the residents, and in 43/128 (34 %) of the data sets collected by the research nurse (p < 0.001). The weighted analysis revealed that residents made more mistakes in 30/44 of collected data items, whereas the research nurse did worse only in 1/44 data items. CONCLUSION: This study shows that the quality of data obtained by our resident physicians was worse than the quality of data obtained by our dedicated research nurse.

Clinical Nursing Research↗

PubMed2002

Quality of data collected for severity of illness scores in the Dutch National Intensive Care Evaluation (NICE) registry.

OBJECTIVE: To analyse the quality of data used to measure severity of illness in the Dutch National Intensive Care Evaluation (NICE) registry, after implementation of quality improving procedures. DESIGN: Data were re-abstracted from the paper records of patients or the Patient Data Management System and compared to the data contained in the registry. The re-abstracted data were considered to be the gold standard. SETTING: ICUs of nine Dutch hospitals that had been collecting data for the NICE registry for at least 1 year. MEASUREMENT AND RESULTS: The mean percentages of inaccurate and incomplete data, per hospital, over all variables, were 6.1%+/-4.4 (SD) and 2.7%+/-4.4 (SD), respectively. The mean difference in severity of illness scores between registry data and re-abstracted data was 0.2 points for APACHE II and 0.4 points for SAPS II. The mean difference in predicted mortality according to APACHE II and SAPS II between registry data and re-abstracted data was 0.4% and 0.02%, respectively. CONCLUSIONS: The current data quality of the NICE registry is good and justifies evaluative research. These positive results might be explained by the implementation of several quality assurance procedures in the NICE registry, such as training and automatic data checks. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s00134-002-1272-z

APACHE↗

PubMed2005

Evaluation of criteria used to assess the quality of aquatic toxicity data.

Good quality toxicity data underpins robust hazard and risk assessments in aquatic systems and the derivation of water quality guidelines for ecosystems. Hence, an objective scheme to assess the quality of toxicity data forms an important part of this process. The variation of scores from 2 research papers using the Australasian ecotoxicity database (AED) quality assessment scheme was evaluated by 23 ecotoxicologists. The results showed that the quality class that the assessors gave each paper varied by less than 10% when compared with a quality score agreed a priori between the authors of this study. It was determined that the majority of the variation in each assessment was due to ambiguous or poorly written assessment criteria, information that was difficult to find, or information in the paper that was overlooked by the assessor. This led to refinements of the assessment criteria in the AED, which resulted in a 16% improvement (i.e., reduction) in the mean variation of scores for the 2 papers when compared with the a priori scores. The improvement in consensus among different assessors evaluating the same research papers suggests that the data quality assessment scheme proposed in this article provides a more robust scheme for assessing the quality of aquatic toxicity data than methods currently available.

Animals↗