Quality of Data Entry Using Single Entry, Double Entry and Automated Forms Processing–An Example Based on a Study of Patient-Reported Outcomes

Background: The clinical and scientific usage of patient-reported outcome measures is increasing in the health services. Often paper forms are used. Manual double entry of data is defined as the definitive gold standard for transferring data to an electronic format, but the process is laborious. Automated forms processing may be an alternative, but further validation is warranted. Methods: 200 patients were randomly selected from a cohort of 5777 patients who had previously answered two different questionnaires. The questionnaires were scanned using an automated forms processing technique, as well as processed by single and double manual data entry, using the EpiData Entry data entry program. The main outcome measure was the proportion of correctly entered numbers at question, form and study level. Results: Manual double-key data entry (error proportion per 1000 fields = 0.046 (95 % CI: 0.001–0.258)) performed better than single-key data entry (error proportion per 1000 fields = 0.370 (95 % CI: 0.160–0.729), (p = 0.020)). There was no statistical difference between Optical Mark Recognition (error proportion per 1000 fields = 0.046 (95 % CI: 0.001–0.258)) and double-key data entry (p = 1.000). With the Intelligent Character Recognition method, there was no statistical difference compared to single-key data entry (error proportion per 1000 fields = 6.734 (95 % CI: 0.817–24.113), (p = 0.656)), as well as double-key data entry (error proportion per 1000 fields = 3.367 (95 % CI: 0.085–18.616)), (p = 0.319))

Delta neutrophil index as an early marker of disease severity in critically ill patients with sepsis

BACKGROUND: The immature granulocyte count has been reported to be a marker of infection and sepsis. The difference in leukocyte subfractions (delta neutrophil index, DNI) in ADVIA 2120 reflects the fraction of circulating immature granulocytes in the blood. This study evaluated the clinical utility of DNI as a severity and prediction marker in critically ill patients with sepsis. METHODS: One hundred and three patients admitted to the medical intensive care unit with sepsis were studied. DNI (the difference in leukocyte subfractions identified by myeloperoxidase and nuclear lobularity channels) was determined using a specific blood cell analyzer. RESULTS: Forty four patients (42.7%) were diagnosed with severe sepsis/septic shock. Overt disseminated intravascular coagulation (DIC) occurred in 40 (38.8%). DNI was significantly higher in patients with severe sepsis/septic shock and overt DIC than in patients without (p 6.5% was a better indicator of severe sepsis/septic shock than C-reactive protein, lactate, white blood cell count, and absolute neutrophil count (sensitivity, 81.3%; specificity, 91.0%; positive predictive value, 88.6%; and negative predictive value, 84.7%). In 36 (82%) of the 44 patients with severe sepsis/septic shock, DNI values were already elevated up to 12 hours before the onset of organ/circulatory failure. CONCLUSIONS: DNI may be used as a marker of disease severity in critically ill patients with sepsis. High levels of DNI may help to identify patients with an impending risk of developing severe sepsis/septic shock.ope

A MATLAB app to assess, compare and validate new methods against their benchmarks

Emerging technologies for physiological signals and data collection enable the monitoring of patient health and well-being in real-life settings. This re-quires novel methods and tools to compare the validity of this kind of in-formation with that acquired in controlled environments using more costly and sophisticated technologies. In this paper, we describe a method and a MATLAB tool that relies on a standard sequence of statistical tests to com-pare features obtained using novel techniques with those acquired by means of benchmark procedures. After introducing the key steps of the proposed statistical analysis method, this paper describes its implementation in a MATLAB app, developed to support researchers in testing the extent to which a set of features, captured with a new methodology, can be considered a valid surrogate of that acquired employing gold standard techniques. An example of the application of the tool is provided in order to validate the method and illustrate the graphical user interface (GUI). The app develop-ment in MATLAB aims to improve its accessibility, foster its rapid adoption among the scientific community and its scalability into wider MATLAB tools

Evaluation of a website providing information on regional health care services for patients with rheumatoid arthritis: an observational study

Studies on the effectiveness of information provision for patients with arthritis through the Internet are scarce. This study aimed to describe rheumatoid arthritis (RA) patients’ knowledge and information needs before and after launching a website providing information on regional health care services for patients with rheumatic conditions. The intervention consisted of a weekly updated website comprising practical information on regional health care services for patients with arthritis. In addition, patients were offered information leaflets and an information meeting. Before (T1) and 24 months after (T2) the website was launched, a random sample of 400 RA patients filled in a questionnaire regarding knowledge and information need (scores 0–18) about accessibility and contents of 18 regional health care services. Two hundred and fifty-one patients returned the questionnaire (response rate 63%) at T1 and 200 patients (50%) at T2, respectively, with 160 paired observations (112 females (70%), mean age 60.4 years (SD 9.9)). The total score for insufficient knowledge about contents decreased from 9.3 (SD 4.9) to 8.5 (SD 4.8; p = 0.03) and for accessibility from 8.6 (SD 4.7) to 8.4 (SD 4.9; p = 0.59). Total score for information need about contents decreased from 4.2 (SD 4.5) to 1.9 (SD 2.9; p < 0.01) and for accessibility from 3.6 (SD 4.5) to 1.4 (SD 2.4; p < 0.01) (paired t-tests)