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Can the use of digital algorithms improve quality care? An example from Afghanistan.

ABSTRACT: BACKGROUND:Quality of care is a difficult parameter to measure. With the introduction of digital algorithms based on the Integrated Management of Childhood Illness (IMCI), we are interested to understand if the adherence to the guidelines improved for a better quality of care for children under 5 years old. METHODS:More than one year after the introduction of digital algorithms, we carried out two cross sectional studies to assess the improvements in comparison with the situation prior to the implementation of the project, in two Basic Health Centres in Kabul province. One survey was carried out inside the consultation room and was based on the direct observation of 181 consultations of children aged 2 months to 5 years old, using a checklist completed by a senior physicians. The second survey queried 181 caretakers of children outside the health facility for their opinion about the consultation carried out through the tablet and prescriptions and medications given. RESULTS:We measured the quality of care as adherence to the IMCI's guidelines. The study evaluated the quality of the physical examination and the therapies prescribed with a special attention to antibiotic prescription. We noticed a dramatic improvement (p<0.05) of several indicators following the introduction of digital algorithms. The baseline physical examination was appropriate only for 23.8% [IC% 19.9-28.1] of the patients, 34.5% [IC% 30.0-39.2] received a correct treatment and 86.1% [IC% 82.4-89.2] received at least one antibiotic. With the introduction of digital algorithms, these indicators statistically improved respectively to 84.0% [IC% 77.9-88.6], >85% and less than 30%. CONCLUSIONS:Our findings suggest that digital algorithms improve quality of care by applying the guidelines more effectively. Our experience should encourage to test this tool in different settings and to scale up its use at province/state level.

SUBMITTER: Bernasconi A

PROVIDER: S-EPMC6261034 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Can the use of digital algorithms improve quality care? An example from Afghanistan.

Bernasconi Andrea A Crabbé François F Raab Martin M Rossi Rodolfo R

PloS one 20181126 11

<h4>Background</h4>Quality of care is a difficult parameter to measure. With the introduction of digital algorithms based on the Integrated Management of Childhood Illness (IMCI), we are interested to understand if the adherence to the guidelines improved for a better quality of care for children under 5 years old.<h4>Methods</h4>More than one year after the introduction of digital algorithms, we carried out two cross sectional studies to assess the improvements in comparison with the situation ...[more]

PMID: 30475833

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Project description:BackgroundAlthough there have been notable improvements in availability and utilization of maternal health care in Afghanistan over the last few decades, risk of maternal mortality remains very high. Previous studies have highlighted gaps in quality of emergency obstetric and newborn care practices, however, little is known about the quality of routine intrapartum care at health facilities in Afghanistan.MethodsWe analyzed a subset of data from the 2016 Afghanistan Maternal and Newborn Health Quality of Care Assessment that comprised of observations of labor, delivery and immediate post-partum care, as well as health facility assessments and provider interviews across all accessible public health facilities with an average of five or more births per day in the preceding year (N = 77). Using the Quality of the Process of Intrapartum and Immediate Postpartum Care index, we calculated a quality of care score for each observation. We conducted descriptive and bivariate analyses and built a multivariate linear regression model to identify facility-level factors associated with quality of care scores.ResultsAcross 665 childbirth observations, low quality of care was observed such that no health facility type received an average quality score over 56%. The multivariate regression model indicated that availability of routine labor and delivery supplies, training in respectful maternity care, perceived gender equality for training opportunities, recent supervision, and observation during supervision have positive, statistically significant associations with quality of care.ConclusionsQuality of routine intrapartum care at health facilities in Afghanistan is concerningly low. Our analysis suggests that multi-faceted interventions are needed to address direct and indirect contributors to quality of care including clinical care practices, attention to client experiences during labor and childbirth, and attention to staff welfare and opportunities, including gender equality within the health workforce.

Project description:BACKGROUND:Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients' health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database. METHODS:EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016-2018) and having hypertension according to a validated definition were included (n?=?48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey. RESULTS:Among those with hypertension, 40.8% (n?=?19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category. CONCLUSION:Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Project description:OBJECTIVE:To test effects on care quality of Chronic Care Model-based Parkinson disease (PD) management. METHODS:This 2-group stratified randomized trial involved 328 veterans with PD in southwestern United States. Guided care management, led by PD nurses, was compared to usual care. Primary outcomes were adherence to 18 PD care quality indicators. Secondary outcomes were patient-centered outcome measures. Data sources were telephone survey and electronic medical record (EMR). Outcomes were analyzed as intent-to-treat comparing initial and final survey and repeated-measures mixed-effects models. RESULTS:Average age was 71 years; 97% of participants were male. Mean proportion of participants receiving recommended PD care indicators was significantly higher for the intervention than for usual care (0.77 vs 0.58) (mean difference 0.19, 95% confidence interval [CI] 0.16, 0.22). Of 8 secondary outcomes, the only significant difference of the changes over time was in the positive Patient Health Questionnaire-2 depression screen for intervention minus usual care (-11.52 [95% CI -20.42, -2.62]). CONCLUSION:A nurse-led chronic care management intervention, Care Coordination for Health Promotion and Activities in Parkinson's Disease (CHAPS), substantially increased adherence to PD quality of care indicators among veterans with PD, as documented in the EMR. Of 8 secondary outcomes assessed, a screening measure for depressive symptomatology was the only measure that was better in the intervention compared to usual care. More telephone calls in CHAPS were the only utilization difference over usual care. While CHAPS appears promising for improving PD care, additional iterative research is needed to refine the CHAPS model in routine clinical care so that it measurably improves patient-centered outcomes (NCT01532986). CLASSIFICATION OF EVIDENCE:This study provides Class I evidence that for patients with PD, CHAPS increased adherence to PD quality of care indicators.

Project description:BackgroundAccurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments.MethodsWe describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings.ResultsThe proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD.ConclusionsWe provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge.

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Can the use of digital algorithms improve quality care? An example from Afghanistan.

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Can the use of digital algorithms improve quality care? An example from Afghanistan.

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