Improving the quality of data entry in a low-budget head injury database

A novel method to assess data quality in large medical registries and databases

BACKGROUND

There is no gold standard to assess data quality in large medical registries. Data auditing may be impeded by data protection regulations.

OBJECTIVE

To explore the applicability and usefulness of funnel plots as a novel tool for data quality control in critical care registries.

METHOD

The Swiss ICU-Registry from all 77 certified adult Swiss ICUs (2014 and 2015) was subjected to quality assessment (completeness/accuracy). For the analysis of accuracy, a list of logical rules and cross-checks was developed. Type and number of errors (true coding errors or implausible data) were calculated for each ICU, along with noticeable error rates (>mean + 3 SD in the variable's summary measure, or >99.8% CI in the respective funnel-plot).

RESULTS

We investigated 164 415 patient records with 31 items each (37 items: trauma diagnosis). Data completeness was excellent; trauma was the only incomplete item in 1495 of 9871 records (0.1%, 0.0%-0.6% [median, IQR]). In 15 572 patients records (9.5%), we found 3121 coding errors and 31 265 implausible situations; the latter primarily due to non-specific information on patients' provenance/diagnosis or supposed incoherence between diagnosis and treatments. Together, the error rate was 7.6% (5.9%-11%; median, IQR).

CONCLUSIONS

The Swiss ICU-Registry is almost complete and data quality seems to be adequate. We propose funnel plots as suitable, easy to implement instrument to assist in quality assurance of such a registry. Based on our analysis, specific feedback to ICUs with special-cause variation is possible and may promote such ICUs to improve the quality of their data.

Minimum data set (MDS) based trauma registry, is the data adequate? An evidence-based study from Odisha, India

BACKGROUND

In majority of the low- and middle-income countries (LMICs), the societal cost of injuries are alarming. The severity and magnitude of the road traffic injuries (RTI) in India are not estimated accurately due to the lack of availability of data. The data are limited on the aspects such as demographics, cause, severity of injury, processes of care, and the final outcome of injuries. This study aimed to determine the feasibility of setting up a sustainable trauma registry in Odisha, India, and to determine the demographics, mechanism, severity, and outcomes of injury reported to the facilities/hospital.

MATERIALS AND METHODS

A prospective observational study was conducted at Srirama Chandra Bhanja Medical College and Hospital (SCB-MCH), Cuttack, India. Injured patients who reported/admitted to the emergency department were observed, and data were collected by using a minimum data set (MDS) developed by the World Health Organization (WHO). Data were collected for a period of one month in June 2015. Observations were collected on 20 variables. The completeness of data collection ranged from 60% (19 variables) to 70% (23 variables) out of total 33 variables.

RESULTS

This study uses 145 cases of injury reported in SCB-MCH. Out of the total reported population at the trauma registry, about 21% were females. Nearly 45% of the injury occurred on road/street. RTI accounted for 36.6% of injury. Out of the total admitted cases, 2.8% died in the emergency department, 11% were discharged to home, and 7.6% left against medical advice. Majority of the respondents have reported single injuries (77%). Head injuries were more common and severe among majority of the reported cases (44.1%), followed by neck injury (28.3%) and chest (15.9%).

CONCLUSIONS

This study indicates the challenges in obtaining complete data on injury. Data were missing in terms of admission, discharge, and Glasgow Comma Scale (GCS) among the studied population. This study suggests that individual GCS scoring should be done instead of total GCS scoring in each trauma patient. By collection and storage of adequate data, better policy decisions can be implemented, which will minimize and prevent trauma cases and maximize the utilization of the available resources.

Classifying, measuring and improving the quality of data in trauma registries: A review of the literature

INTRODUCTION

Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries.

METHODS

A scoping review of the literature was performed. Databases were searched using the term "trauma registry" and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture.

RESULTS

Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n=47) were checks against other datasets (n=18) and checks of injury coding (n=17). The most frequently described methods for measuring data completeness (n=47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n=29). The most frequently described methods for measuring data capture (n=37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n=24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted.

CONCLUSION

The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised.

Data validation and other strategies for data entry

Data entry can result in errors that cause analytic problems and delays in disseminating research. Invalid responses can lead to incorrect statistics and statistical conclusions. The purpose of this article is to provide researchers some basic strategies for avoiding out-of-range data entry errors and streamlining data collection. This article identifies some basic strategies using Microsoft® Excel, which is an inexpensive method of data entry that can be used when research budgets are constrained. Data files can be structured so that out-of-range values cannot be entered. When string variables are entered, researchers may be inconsistent in the way they code responses. Data validation can be accomplished through the use of restricting response options and skipping items can be avoided by using count functions to tabulate the number of valid responses. We also discuss advantages and disadvantages of several methods of data entry, including using web-based data entry and relational databases.

Data Quality Improvement in Clinical Databases Using Statistical Quality Control: Review and Case Study

Ensuring the quality of data being collected in clinical and medical contexts is a concern for data managers and users. Quality assurance frameworks, systematic audits, and correction procedures have been proposed to enhance the accuracy and completeness of databases. Following an overview of the undertaken approaches, particularly statistical methods, the authors promote acceptance sampling plans (ASPs) and statistical process control (SPC) tools, including control charts and root cause analysis, as the technical core of the data quality improvement mechanism. They review ASP and SPC techniques and discuss their implementation in data quality evaluation and improvement. Two case studies are presented in which the authors apply some of the techniques to databases maintained by a local hospital. Finally, guidelines are proposed for which techniques are appropriate with regard to dataflow and database specifications.

Error rates in a clinical data repository: lessons from the transition to electronic data transfer--a descriptive study

OBJECTIVE

Data errors are a well-documented part of clinical datasets as is their potential to confound downstream analysis. In this study, we explore the reliability of manually transcribed data across different pathology fields in a prostate cancer database and also measure error rates attributable to the source data.

DESIGN

Descriptive study.

SETTING

Specialist urology service at a single centre in metropolitan Victoria in Australia.

PARTICIPANTS

Between 2004 and 2011, 1471 patients underwent radical prostatectomy at our institution. In a large proportion of these cases, clinicopathological variables were recorded by manual data-entry. In 2011, we obtained electronic versions of the same printed pathology reports for our cohort. The data were electronically imported in parallel to any existing manual entry record enabling direct comparison between them.

OUTCOME MEASURES

Error rates of manually entered data compared with electronically imported data across clinicopathological fields.

RESULTS

421 patients had at least 10 comparable pathology fields between the electronic import and manual records and were selected for study. 320 patients had concordant data between manually entered and electronically populated fields in a median of 12 pathology fields (range 10-13), indicating an outright accuracy in manually entered pathology data in 76% of patients. Across all fields, the error rate was 2.8%, while individual field error ranges from 0.5% to 6.4%. Fields in text formats were significantly more error-prone than those with direct measurements or involving numerical figures (p<0.001). 971 cases were available for review of error within the source data, with figures of 0.1-0.9%.

CONCLUSIONS

While the overall rate of error was low in manually entered data, individual pathology fields were variably prone to error. High-quality pathology data can be obtained for both prospective and retrospective parts of our data repository and the electronic checking of source pathology data for error is feasible.

Traumatic brain injury in an aging population

The epidemiology of traumatic brain injury (TBI) is changing in several Western countries, with an increasing proportion of elderly TBI patients admitted to the intensive care unit (ICU). We describe a series of 1366 adult patients admitted to three neuro-ICUs in which 44% of cases were 50 years of age or older. The health status before trauma (rated using the APACHE score) was worse in older patients. In all 604 patients had emergency removal of intracranial masses, with extradural hematomas more frequent in young cases and subdural hematomas more frequent in older patients. Outcomes were classified according to the Glasgow Outcome Scale (GOS) 6 months post-trauma, as favorable (GOS score 4-5), or unfavorable (GOS score 1-3). Favorable outcomes were achieved by 50% of patients, but the proportions of unfavorable outcomes rose with age. Mortality was the main cause of unfavorable outcomes 6 months after injury in older patients. Logistic regression analysis indicates that several parameters independently contributed to outcome, including the motor component of the Glasgow Coma Scale (GCS), pupils, CT findings, and early hypotension. Additionally, the odds ratios were very high for age and health status before TBI. Patients admitted to the ICU are increasingly older, have co-morbidities, and have specific types of intracranial lesions. Early rescue, surgical treatment, and intensive care of these patients may produce excellent results up to the age of 59 years, with favorable outcomes still possible for 39% of cases aged 60-69 years, without an excessive burden of severely disabled patients.

"Summary Page": a novel tool that reduces omitted data in research databases

Background

Data entry errors are common in clinical research databases. Omitted data are of particular concern because they are more common than erroneously inserted data and therefore could potentially affect research findings. However, few affordable strategies for their prevention are available.

Methods

We have conducted a prospective observational study of the effect of a novel tool called "Summary Page" on the frequency of correction of omitted data errors in a radiation oncology research database between July 2008 and March 2009. "Summary Page" was implemented as an optionally accessed screen in the database that visually integrates key fields in the record. We assessed the frequency of omitted data on the example of the Date of Relapse field. We considered the data in this field to be omitted for all records that had empty Date of Relapse field and evidence of relapse elsewhere in the record.

Results

A total of 1,156 records were updated and 200 new records were entered in the database over the study period. "Summary Page" was accessed for 44% of all updated records and for 69% of newly entered records. Frequency of correction of the omitted date of cancer relapse was six-fold higher in records for which "Summary Page" was accessed (p = 0.0003).

Conclusions

"Summary Page" was strongly associated with an increased frequency of correction of omitted data errors. Further, controlled, studies are needed to confirm this finding and elucidate its mechanism of action.

Trauma patients without a trauma diagnosis: the data gap at a level one trauma center

BACKGROUND

Trauma registries may contain records without a codable trauma diagnosis, creating a "data gap" that multiplies the number of invalid registry data fields. We designed an investigation intended to determine the incidence of registry records with noncodable trauma diagnoses, characterize those records, and determine the reasons for inadequate diagnosis data.

METHODS

We used a retrospective cohort design. A query of trauma registry records spanning a 5-year period yielded 129 records with no injury severity score. Each patient's medical record was reviewed, sources of diagnostic information were noted, and diagnoses were categorized.

RESULTS

In 57% of cases, we found documentation that the patient had sustained an injury, but the injury was inadequately documented in the discharge summary. In 19% of cases, although the registry record was valid, the diagnosis was not codable as trauma. In 17% of cases, clinical documentation was adequate, but the diagnosis was inadequately recorded in the trauma registry. In 13% of cases, no traumatic injury was sustained, although the registry record was valid. In 2% of cases, the trauma registry record itself was invalid. In 1% of cases, a coding error occurred. Particularly prominent among records with inadequate discharge documentation were cases of head and spine injury for which there was no radiographic evidence.

CONCLUSIONS

The incidence of records with noncodable diagnoses might best be reduced through improved physician documentation, revision of trauma registry inclusion criteria, increased attention by trauma registrars to key sources of documentation, and direct communication with the attending physician when necessary.

The brain monitoring with Information Technology (BrainIT) collaborative network: data validation results

BACKGROUND

The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology.

MATERIALS AND METHODS

Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category.

FINDINGS

Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found.

CONCLUSIONS

The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised.