Why are structured data different? Relating differences in data representation to the rationale of OpenSDE

Protocolization of multicenter clinical studies in the digital era. Is useful data centralization by a data-manager?

INTRODUCTION

In multicenter studies, the protocolization of data is a critical phase that can generate biases.The objective is to analyze the concordance and reliability of the data obtained in a clinical multicenter study between the protocolization in the center of origin and the centralized protocolization of the data by a data -manager.

METHODS

National multicenter clinical study about an infrequent carcinoma. A double protocolization of the data is carried out: (a) center of origin; and (b) centralized by a data manager: The concordance between the data is analyzed for the global data and for the two groups of the project: (a) study group (Familiar carcinoma, 30 researchers protocolize); (b) control group (Sporadic carcinoma, 4 people protocolize). Interobserver variability is evaluated using Cohen's kappa coefficient.

RESULTS

The study includes a total of 689 patients with carcinoma, 252 in the study group and 437 in the control group. Regarding the concordance analysis of the tumor stage, 2.5% of disagreements were observed and the concordance between people who protocolize was near perfect (Kappa = 0.931). Regarding the evaluation of the recurrence risk, disagreements occurred in 7% of the cases and the concordance was near perfect (Kappa = 0.819). Regarding the sonography evaluation (TIRADS), the disagreements were 6.9% and the concordance was near perfect (Kappa = 0.922). Also, 4.6% of transcription errors were detected.

CONCLUSIONS

In multicenter clinical studies, the centralized data protocolization o by a data-manager seems to present similar results to the direct protocolization in the database in the center of origin.

Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography

Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from (99m)Tc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system.

METHODS

For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians.

RESULTS

RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84).

CONCLUSION

We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.

A structured registration program can be validly used for quality assessment in general practice

BACKGROUND

Patient information, medical history, clinical outcomes and demographic information, can be registered in different ways in registration programs. For evaluation of diabetes care, data can easily be extracted from a structured registration program (SRP). The usability of data from this source depends on the agreement of this data with that of the usual data registration in the electronic medical record (EMR).Aim of the study was to determine the comparability of data from an EMR and from an SRP, to determine whether the use of SRP data for quality assessment is justified in general practice.

METHODS

We obtained 196 records of diabetes mellitus patients in a sample of general practices in the Netherlands. We compared the agreement between the two programs in terms of laboratory and non-laboratory parameters. Agreement was determined by defining accordance between the programs in absent and present registrations, accordance between values of registrations, and whether the differences found in values were also a clinically relevant difference.

RESULTS

No differences were found in the occurrence of registration (absent/present) in the SRP and EMR for all the laboratory parameters. Smoking behaviour, weight and eye examination were registered significantly more often in the SRP than in the EMR. In the EMR, blood pressure was registered significantly more often than in the SRP. Data registered in the EMR and in the SRP had a similar clinical meaning for all parameters (laboratory and non-laboratory).

CONCLUSIONS

Laboratory parameters showed good agreement and non-laboratory acceptable agreement of the SRP with the EMR. Data from a structured registration program can be used validly for research purposes and quality assessment in general practice.

Pediatric aspects of inpatient health information technology systems

US adoption of health information technology as a path to improved quality of patient care (effectiveness, safety, timeliness, patient-centeredness, efficiency, and equity) has been promoted by the medical community. Children and infants (especially those with special health care needs) are at higher risk than are adults for medical errors and their consequences (particularly in environments in which children are not the primary patient population). However, development and adoption of health information technology tools and practices that promote pediatric quality and patient safety are lagging. Two inpatient clinical processes-medication delivery and patient care transitions-are discussed in terms of health information technology applications that support them and functions that are important to pediatric quality and safety. Pediatricians and their partners (pediatric nurses, pharmacists, etc) must develop awareness of technical and adaptive issues in adopting these tools and collaborate with organizational leaders and developers as advocates for the best interests and safety of pediatric patients. Pediatric health information technology adoption cannot be considered in terms of applications (such as electronic health records or computerized physician order entry) alone but must be considered globally in terms of technical (health information technology applications), organizational (structures and workflows of care), and cultural (stakeholders) aspects of what is best.

Double-blind control of the data manager doesn't have any impact on data entry reliability and should be considered as an avoidable cost

BACKGROUND

Database systems have been developed to store data from large medical trials and survey studies. However, a reliable data storage system does not guarantee data entering reliability.We aimed to evaluate if double-blind control of the data manager might have any effect on data-reliability. Our secondary aim was to assess the influence of the inserting position in the insertion-sheet on data-entry accuracy and the effectiveness of electronic controls in identifying data-entering mistakes.

METHODS

A cross-sectional survey and single data-manager data entry.Data from PACMeR_02 survey, which had been conducted within a framework of the SESy-Europe project (PACMeR_01.4), were used as substrate for this study. We analyzed the electronic storage of 6,446 medical charts. We structured data insertion in four sequential phases. After each phase, the data stored in the database were tested in order to detect unreliable entries through both computerized and manual random control. Control was provided in a double blind fashion.

RESULTS

Double-blind control of the data manager didn't improve data entry reliability. Entries near the end of the insertion sheet were correlated with a larger number of mistakes. Data entry monitoring by electronic-control was statistically more effective than hand-searching of randomly selected medical records.

CONCLUSION

Double-blind control of the data manager should be considered an avoidable cost. Electronic-control for monitoring of data-entry reliability is suggested.