Clinical audit of COPD patients requiring hospital admissions in Spain: AUDIPOC study.

BACKGROUNDS AUDIPOC is a nationwide clinical audit that describes the characteristics, interventions and outcomes of patients admitted to Spanish hospitals because of an exacerbation of chronic obstructive pulmonary disease (ECOPD), assessing the compliance of these parameters with current international guidelines. The present study describes hospital resources, hospital factors related to case recruitment variability, patients' characteristics, and adherence to guidelines. METHODOLOGY/PRINCIPAL FINDINGS An organisational database was completed by all participant hospitals recording resources and organisation. Over an 8-week period 11,564 consecutive ECOPD admissions to 129 Spanish hospitals covering 70% of the Spanish population were prospectively identified. At hospital discharge, 5,178 patients (45% of eligible) were finally included, and thus constituted the audited population. Audited patients were reassessed 90 days after admission for survival and readmission rates. A wide variability was observed in relation to most variables, hospital adherence to guidelines, and readmissions and death. Median inpatient mortality was 5% (across-hospital range 0-35%). Among discharged patients, 37% required readmission (0-62%) and 6.5% died (0-35%). The overall mortality rate was 11.6% (0-50%). Hospital size and complexity and aspects related to hospital COPD awareness were significantly associated with case recruitment. Clinical management most often complied with diagnosis and treatment recommendations but rarely (<50%) addressed guidance on healthy life-styles. CONCLUSIONS/SIGNIFICANCE The AUDIPOC study highlights the large across-hospital variability in resources and organization of hospitals, patient characteristics, process of care, and outcomes. The study also identifies resources and organizational characteristics associated with the admission of COPD cases, as well as aspects of daily clinical care amenable to improvement.

Preanalytical mistakes in samples from primary care patients.

BACKGROUND Preanalytical mistakes (PAMs) in samples usually led to rejection upon arrival to the clinical laboratory. However, PAMs might not always be detected and result in clinical problems. Thus, PAMs should be minimized. We detected PAMs in samples from Primary Health Care Centres (PHCC) served by our central laboratory. Thus, the goal of this study was to describe the number and types of PAMs, and to suggest some strategies for improvement. METHODS The presence of PAMs, as sample rejection criteria, in samples submitted from PHCC to our laboratory during October and November 2007 was retrospectively analysed. RESULTS Overall, 3885 PAMs (7.4%) were detected from 52,669 samples for blood analyses. This included missed samples (n=1763; 45.4% of all PAMs, 3.3% of all samples), haemolysed samples (n=1408; 36.2% and 2.7%, respectively), coagulated samples (n=391; 10% and 0.7%, respectively), incorrect sample volume (n=110; 2.8% and 0.2%, respectively), and others (n=213; 5.5% and 0.4%, respectively). For urine samples (n=18,852), 1567 of the samples were missing (8.3%). CONCLUSIONS We found the proportion of PAMs in blood and urine samples to be 3-fold higher than that reported in the literature. Therefore, strategies for improvement directed towards the staff involved, as well as an exhaustive audit of preanalytical process are needed. To attain this goal, we first implemented a continued education programme, financed by our Regional Health Service and focused in Primary Care Nurses.

Infornut® Process; improves accessibility to diagnosis and nutritional support for the malnourished hospitalized patient; impact on management indicators; two-year assessment.

Introduction: The high prevalence of disease-related hospital malnutrition justifies the need for screening tools and early detection in patients at risk for malnutrition, followed by an assessment targeted towards diagnosis and treatment. At the same time there is clear undercoding of malnutrition diagnoses and the procedures to correct it Objectives: To describe the INFORNUT program/ process and its development as an information system. To quantify performance in its different phases. To cite other tools used as a coding source. To calculate the coding rates for malnutrition diagnoses and related procedures. To show the relationship to Mean Stay, Mortality Rate and Urgent Readmission; as well as to quantify its impact on the hospital Complexity Index and its effect on the justification of Hospitalization Costs. Material and methods: The INFORNUT® process is based on an automated screening program of systematic detection and early identification of malnourished patients on hospital admission, as well as their assessment, diagnoses, documentation and reporting. Of total readmissions with stays longer than three days incurred in 2008 and 2010, we recorded patients who underwent analytical screening with an alert for a medium or high risk of malnutrition, as well as the subgroup of patients in whom we were able to administer the complete INFORNUT® process, generating a report for each.

Identification of factors associated with diagnostic error in primary care.

BACKGROUND Missed, delayed or incorrect diagnoses are considered to be diagnostic errors. The aim of this paper is to describe the methodology of a study to analyse cognitive aspects of the process by which primary care (PC) physicians diagnose dyspnoea. It examines the possible links between the use of heuristics, suboptimal cognitive acts and diagnostic errors, using Reason's taxonomy of human error (slips, lapses, mistakes and violations). The influence of situational factors (professional experience, perceived overwork and fatigue) is also analysed. METHODS Cohort study of new episodes of dyspnoea in patients receiving care from family physicians and residents at PC centres in Granada (Spain). With an initial expected diagnostic error rate of 20%, and a sampling error of 3%, 384 episodes of dyspnoea are calculated to be required. In addition to filling out the electronic medical record of the patients attended, each physician fills out 2 specially designed questionnaires about the diagnostic process performed in each case of dyspnoea. The first questionnaire includes questions on the physician's initial diagnostic impression, the 3 most likely diagnoses (in order of likelihood), and the diagnosis reached after the initial medical history and physical examination. It also includes items on the physicians' perceived overwork and fatigue during patient care. The second questionnaire records the confirmed diagnosis once it is reached. The complete diagnostic process is peer-reviewed to identify and classify the diagnostic errors. The possible use of heuristics of representativeness, availability, and anchoring and adjustment in each diagnostic process is also analysed. Each audit is reviewed with the physician responsible for the diagnostic process. Finally, logistic regression models are used to determine if there are differences in the diagnostic error variables based on the heuristics identified. DISCUSSION This work sets out a new approach to studying the diagnostic decision-making process in PC, taking advantage of new technologies which allow immediate recording of the decision-making process.