Uncontrolled diabetes: A difficult mother or a mother in difficulty?

Young children with diabetes (YCD) are a particularly vulnerable group because they are reliant on adult carers in their management. Diabetes treating teams (DTT) have a responsibility towards YCD targeting good glycemic control (GC) to improve quality of life and reduce risk of complications. It can be difficult, however, in occasions to balance between providing support for struggling families and considering safeguarding YCD who are not well looked after by carers in their management. We report a 6-year-old girl with type 1 diabetes with HbA1c ranged between 10.7% and 15.7%. A number of social factors have influenced her diabetes control including parental separation, maternal mental health concerns and lack of family support. Each time, these issues have been addressed, and also when grandparents were involved, a transient short-lived improvement in GC was observed. However, there were always ongoing concerns about mother's lack of engagement with the DTT. Similar cases continue to pose significant challenges for DTT, worldwide. A balance should be kept between providing adequate support for such families against a possible need for safeguarding YCD. Using a patient centered approach, if there is no improvement in GC despite taking all measures to support mothers or families who struggle with their YCD management, it becomes difficult to justify not involving the safeguarding team and social services.

Do poor patients suffer from inaccurate diagnoses more than well-to-do patients? A randomized control trial.

BACKGROUND: Poor patients have greater morbidity and die up to 10 years earlier than patients who have higher socio-economic status. These findings are often attributed to differences in life-style between groups. The present study aimed at investigating the extent to which physicians contribute to the effect by providing relative poorer care, resulting in relative neglect in terms of time spent with a poor patient and more inaccurate diagnoses. METHODS: A randomised experiment with 45 internal medicine residents. Doctors diagnosed 12 written clinical vignettes that were exactly the same except for the description of the patients' socio-economic status. Each participant diagnosed four of the vignettes in a poor-patient version, four in a rich-patient version, and four in a version that did not contain socio-economic markers, in a balanced within-subjects incomplete block design. Main measurements were: diagnostic accuracy scores and time spent on diagnosis. RESULTS: Mean diagnostic accuracy scores (range 0-1) did not significantly differ among the conditions of the experiment (for poor patients: 0.48; for rich patients: 0.52; for patients without socio-economic markers: 0.54; p > 0.05). While confronted with patients not presenting with socio-economic background information, the participants spent significantly less time-to-diagnosis ((for poor patients: 168 s; for rich patients: 176 s; for patients without socio-economic markers: 151 s; p < 0.01), however due to the fact that the former vignettes were shorter. CONCLUSION: There is no reason to believe that physicians are prejudiced against poor patients and therefore treat them differently from rich patients or patients without discernible socio-economic background.

Failure Mode and Effect Analysis (FMEA) may enhance implementation of clinical practice guidelines: An experience from the Middle East.

RATIONALE, AIMS, AND OBJECTIVES: Implementation of clinical practice guidelines (CPGs) has been shown to reduce variation in practice and improve health care quality and patients' safety. There is a limited experience of CPG implementation (CPGI) in the Middle East. The CPG program in our institution was launched in 2009. The Quality Management department conducted a Failure Mode and Effect Analysis (FMEA) for further improvement of CPGI. METHODS: This is a prospective study of a qualitative/quantitative design. Our FMEA included (1) process review and recording of the steps and activities of CPGI; (2) hazard analysis by recording activity-related failure modes and their effects, identification of actions required, assigned severity, occurrence, and detection scores for each failure mode and calculated the risk priority number (RPN) by using an online interactive FMEA tool; (3) planning: RPNs were prioritized, recommendations, and further planning for new interventions were identified; and (4) monitoring: after reduction or elimination of the failure mode. The calculated RPN will be compared with subsequent analysis in post-implementation phase. RESULTS: The data were scrutinized from a feedback of quality team members using a FMEA framework to enhance the implementation of 29 adapted CPGs. The identified potential common failure modes with the highest RPN (≥ 80) included awareness/training activities, accessibility of CPGs, fewer advocates from clinical champions, and CPGs auditing. Actions included (1) organizing regular awareness activities, (2) making CPGs printed and electronic copies accessible, (3) encouraging senior practitioners to get involved in CPGI, and (4) enhancing CPGs auditing as part of the quality sustainability plan. CONCLUSION: In our experience, FMEA could be a useful tool to enhance CPGI. It helped us to identify potential barriers and prepare relevant solutions.