Clinical essentialising: a qualitative study of doctors’ medical and moral practice

While certain substantial moral dilemmas in health care have been given much attention, like abortion, euthanasia or gene testing, doctors rarely reflect on the moral implications of their daily clinical work. Yet, with its aim to help patients and relieve suffering, medicine is replete with moral decisions. In this qualitative study we analyse how doctors handle the moral aspects of everyday clinical practice. About one hundred consultations were observed, and interviews conducted with fifteen clinical doctors from different practices. It turned out that the doctors’ approach to clinical cases followed a rather strict pattern across specialities, which implied transforming patients’ diverse concerns into specific medical questions through a process of ‘essentialising’: Doctors broke the patient’s story down, concretised the patient’s complaints and categorised the symptoms into a medical sense. Patients’ existential meanings were removed, and the focus placed on the patients’ functioning. By essentialising, doctors were able to handle a complex and ambiguous reality, and establish a medically relevant problem. However, the process involved a moral as well as a practical simplification. Overlooking existential meanings and focusing on purely functional aspects of patients was an integral part of clinical practice and not an individual flaw. The study thus questions the value of addressing doctors’ conscious moral evaluations. Yet doctors should be aware that their daily clinical work systematically emphasises beneficence at the expense of others—that might be more important to the patient

The use of adherence aids by adults with diabetes: A cross-sectional survey

BACKGROUND: Adherence with medication taking is a major barrier to physiologic control in diabetes and many strategies for improving adherence are in use. We sought to describe the use of mnemonic devices and other adherence aids by adults with diabetes and to investigate their association with control of hyperglycemia, hyperlipidemia and hypertension. METHODS: Cross sectional survey of diabetic adults randomly selected from Primary Care practices in the Vermont Diabetes Information System. We used linear regression to examine the associations between the use of various aids and physiologic control among subjects who used oral agents for hyperglycemia, hypercholesterolemia, and hypertension. RESULTS: 289 subjects (mean age 65.4 years; 51% female) used medications for all three conditions. Adherence aids were reported by 80%. The most popular were day-of-the-week pill boxes (50%), putting the pills in a special place (41%), and associating pill taking with a daily event such as a meal, TV show, or bedtime (11%). After adjusting for age, sex, marital status, income, and education, those who used a special place had better glycemic control (A1C -0.36%; P = .04) and systolic blood pressure (-5.9 mm Hg; P = .05) than those who used no aids. Those who used a daily event had better A1C (-0.56%; P = .01) than patients who used no aids. CONCLUSION: Although adherence aids are in common use among adults with diabetes, there is little evidence that they are efficacious. In this study, we found a few statistically significant associations with adherence aids and better diabetes control. However, these findings could be attributed to multiple comparisons or unmeasured confounders. Until more rigorous evaluations are available, it seems reasonable to recommend keeping medicines in a special place for diabetic adults prescribed multiple medications

Suitability Of Nitisinone In Alkaptonuria 1 (SONIA 1): an international, multicentre, randomised, open-label, no-treatment controlled, parallel-group, dose-response study to investigate the effect of once daily nitisinone on 24-h urinary homogentisic acid excretion in patients with alkaptonuria after 4 weeks of treatment.

BACKGROUND: Alkaptonuria (AKU) is a serious genetic disease characterised by premature spondyloarthropathy. Homogentisate-lowering therapy is being investigated for AKU. Nitisinone decreases homogentisic acid (HGA) in AKU but the dose-response relationship has not been previously studied. METHODS: Suitability Of Nitisinone In Alkaptonuria 1 (SONIA 1) was an international, multicentre, randomised, open-label, no-treatment controlled, parallel-group, dose-response study. The primary objective was to investigate the effect of different doses of nitisinone once daily on 24-h urinary HGA excretion (u-HGA24) in patients with AKU after 4 weeks of treatment. Forty patients were randomised into five groups of eight patients each, with groups receiving no treatment or 1 mg, 2 mg, 4 mg and 8 mg of nitisinone. FINDINGS: A clear dose-response relationship was observed between nitisinone and the urinary excretion of HGA. At 4 weeks, the adjusted geometric mean u-HGA24 was 31.53 mmol, 3.26 mmol, 1.44 mmol, 0.57 mmol and 0.15 mmol for the no treatment or 1 mg, 2 mg, 4 mg and 8 mg doses, respectively. For the most efficacious dose, 8 mg daily, this corresponds to a mean reduction of u-HGA24 of 98.8% compared with baseline. An increase in tyrosine levels was seen at all doses but the dose-response relationship was less clear than the effect on HGA. Despite tyrosinaemia, there were no safety concerns and no serious adverse events were reported over the 4 weeks of nitisinone therapy. CONCLUSIONS: In this study in patients with AKU, nitisinone therapy decreased urinary HGA excretion to low levels in a dose-dependent manner and was well tolerated within the studied dose range. TRIAL REGISTRATION NUMBER: EudraCT number: 2012-005340-24. Registered at ClinicalTrials.gov: NCTO1828463

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract