Hybrid journal (It can contain Open Access articles) ISSN (Print) 1353-4505 - ISSN (Online) 1464-3677 Published by Oxford University Press[409 journals]
Authors:Perren A; Cerutti B, Kaufmann M, et al. Pages: 1 - 7 Abstract: AbstractBackgroundThere is no gold standard to assess data quality in large medical registries. Data auditing may be impeded by data protection regulations.ObjectiveTo explore the applicability and usefulness of funnel plots as a novel tool for data quality control in critical care registries.MethodThe 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).ResultsWe 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).ConclusionsThe 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. PubDate: Fri, 04 Jan 2019 00:00:00 GMT DOI: 10.1093/intqhc/mzy249 Issue No:Vol. 31, No. 7 (2019)
Authors:Savage C; Bjessmo S, Borisenko O, et al. Pages: 30 - 36 Abstract: AbstractObjectiveTo explore how the See-and-Treat concept can be applied in primary care and its effect on volume and productivity.DesignAn explanatory single-case study design with a mixed methods approach and presented according to the SQUIRE 2.0 guidelines.SettingA publicly-funded, private primary care provider within the Stockholm County, which caters to a diverse patient population in terms of ethnicity, religion, socioeconomic status and care needs.ParticipantsCEO, center manager, four physicians, two licensed practical nurses, one medical secretary and one lab assistant.InterventionA See-and-Treat unit was established to offer same-day service for acute unplanned visits. Standardized patient symptom forms were created that allowed patients to self-triage and then enter into a streamlined care process consisting of a quick diagnostic lab and a physician visit.Main Outcome MeasuresVolume, productivity, staff perceptions and patient satisfaction were measured through data on number and type of contacts per 1000 listed patients, visits per physician, observations, interviews and a questionnaire.ResultsA significant decrease in the acute and total number of visits, a continued trend of diminishing telephone contacts, and a non-significant increase in physician productivity. Patients were very satisfied, and staff perceived an improved quality of care.ConclusionsSee-and-Treat appears to be a viable approach for a specific primary care patient segment interested in acute same-day-service. Opening up access and standardizing care made it possible to efficiently address these needs and engage patients. PubDate: Wed, 09 Jan 2019 00:00:00 GMT DOI: 10.1093/intqhc/mzy244 Issue No:Vol. 31, No. 7 (2019)
Authors:Lim M; Lim Y, Teh X, et al. Pages: 37 - 43 Abstract: AbstractObjectiveTo determine the extent of self-management support (SMS) provided to primary care patients with type 2 diabetes (T2D) and hypertension and its associated factors.DesignCross-sectional survey conducted between April and May 2017.SettingForty public clinics in Malaysia.ParticipantsA total of 956 adult patients with T2D and/or hypertension were interviewed.Main Outcome MeasuresPatient experience on SMS was evaluated using a structured questionnaire of the short version Patient Assessment of Chronic Illness Care instrument, PACIC-M11. Linear regression analysis adjusting for complex survey design was used to determine the association of patient and clinic factors with PACIC-M11 scores.ResultsThe overall PACIC-M11 mean was 2.3(SD,0.8) out of maximum of 5. The subscales’ mean scores were lowest for patient activation (2.1(SD,1.1)) and highest for delivery system design/decision support (2.9(SD,0.9)). Overall PACIC-M11 score was associated with age, educational level and ethnicity. Higher overall PACIC-M11 ratings was observed with increasing difference between actual and expected consultation duration [β = 0.01; 95% CI (0.001, 0.03)]. Better scores were also observed among patients who would recommend the clinic to friends and family [β = 0.19; 95% CI (0.03, 0.36)], when health providers were able to explain things in ways that were easy to understand [β = 0.34; 95% CI (0.10, 0.59)] and knew about patients’ living conditions [β = 0.31; 95% CI (0.15, 0.47)].ConclusionsOur findings indicated patients received low levels of SMS. PACIC-M11 ratings were associated with age, ethnicity, educational level, difference between actual and expected consultation length, willingness to recommend the clinic and provider communication skills. PubDate: Fri, 04 Jan 2019 00:00:00 GMT DOI: 10.1093/intqhc/mzy252 Issue No:Vol. 31, No. 7 (2019)
Authors:Islam M; MD, Li Y. Pages: 495 - 496 Abstract: Quality of care and patient’s safety are now recognized globally as a healthcare priority. While adverse events (AEs) are a serious issue related to the patient’s safety, concern has been raised on the quality of care provided globally. It is reported that AEs reckon additional 13–16% costs alone due to only prolonged hospital stay. The annual cost of prolonging hospital stay because of AEs is ~£2 billion in the UK [1]. Moreover, other issues like pain and suffering, loss of independence and productivity of patients or costs of litigation and settlement of medical negligence claims are often ignored while calculating the total economic burden of AEs. An increased number of AEs always have detrimental effects on both patients and healthcare providers including physical and mental harm, reducing credibility of the healthcare system. It is therefore important to identify and measure AEs for prioritizing problems to work on and making sophisticated ideas for better patient care as they generate substantial burden to patients and healthcare providers [2]. Although there is no gold standard for measuring AEs, a significant number of studies used the Harvard Medical Practice Study (HMPS) approach as a standard methodology for measuring AEs [3]. Trigger tools like the global trigger tool (GTT) (introduced by the institute for healthcare improvement) have been developed to identify and measure the AEs. It is an easy and less labor intensive two-stage method of retrospectively manual review of the patient’s chart. Firstly, two nurses individually screen patients’ reports for specific triggers and ascertain AEs regarding these triggers before making any decision. Secondly, physicians verify them based on the standard definition [4]. PubDate: Fri, 08 Nov 2019 00:00:00 GMT DOI: 10.1093/intqhc/mzz077 Issue No:Vol. 31, No. 7 (2019)
Hybrid journal (It can contain Open Access articles)
[409 journals]
