About the Project
It has been demonstrated that used Facebook users’ “likes” can be used to reliably identify their personality characteristics and preferences. This project proposes to characterise GPs’ using their prescribing and diagnostic test ordering decisions. It is intended that this should provide insights into variation in clinical behaviour between clinicians.
Two independent analyses of large datasets of prescribing data have observed prescribing rates of quite different types of drugs are strongly correlated at the level of primary care prescriber. Maust et al found benzodiazepine prescribing was highly correlated with opioid prescribing, but also with antibiotic prescribing and prescribing of high-risk medications for the elderly. This suggests a single underlying construct determines high prescribing or a range of medications. The authors suggested this might be due to an underlying personal characteristic, hypothesising that the physicians’ consultation style, use of education and communication in consultations might be a factor. They noted that female physicians were lower prescribers.
A similar UK study analysed two databases, one of primary care records, the other of prescription reimbursement data. It also found strong correlations at the level of general practice between antibiotic prescribing rates and prescribing rates of all other drugs. These correlations were only modestly attenuated after adjustment for patient characteristics. In the prescription reimbursement database the adjusted regression model of prescription of all other medicines (i.e. everything except antibiotics) explained 56% of the variation in antibiotic prescription and 62.9% of the variation in the primary care database. Rates of antibiotic prescription correlated strongly with prescription rates of specific drug classes unrelated to antibiotics, including: proton pump inhibitors, renin-angiotensin system drugs, antiplatelet drugs, drugs used in nausea and vertigo, and selective serotonin reuptake inhibitors. The authors concluded that propensity to prescribe was the single strongest determinant of antibiotic prescribing.
Previous research has characterised general practitioners (GPs) by their prescribing and diagnostic styles and used this to predict health outcomes in patients. GPs were characterised as having an integrated style, with low diagnostic test, prescription and referral rates; an interventionist style with by high diagnostic test, prescription and referral rates; or a minimal diagnostic style characterised by low diagnostic test rates but high prescription and referral rates. Patients of general practitioners with an integrated style consulted less frequently and self-reported better health.
More widely, it has been observed that investigation of variation in health care use has overlooked the role of style of physician practice.
There is evidence that GP prescribing is influenced by an underlying propensity to prescribe. This may also be true of diagnostic tests. These characteristics may be important determinants of health care use and of patient outcomes. The proposed doctoral thesis aims to will investigate whether clinical prescribing and diagnostic test behaviour can be used to characterise GPs and to predict health care utilisation by patients.
1. Identify a range of index prescribing decisions attributable to individual GPs and develop appropriate measures of prescribing rates for these index prescribing decisions.
2. Identify a range of index diagnostic test ordering decisions attributable to individual GPs and develop appropriate measures of diagnostic test rates for these index diagnostic test decisions.
3. Undertake statistical analysis to determine the number of factors underlying GP propensity to prescribe and to order diagnostic tests
4. Describe the variation in the propensity to prescribe and propensity to order diagnostic tests over time within the same GP and variation by general practice.
5. Investigate the relationship between propensity to prescribe and propensity to order diagnostic tests and health care use (visit frequency) in primary care
Large dataset of anonymised electronic primary care records
Applicants must be able or willing to learn to undertake statistical analysis of large datasets (millions of records) of electronic health records.
Applicants should have some understanding of primary health care in the UK.
This is likely to suit someone with strong analytic skills in epidemiology, statistics, possibly psychometrics or econometrics and an interest in health care.
This will NOT be suitable for a clinical or management graduate who does not have analytic skills (e.g. a degree in statistics or epidemiology).
2 Maust DT, Lin LA, Blow FC, Marcus SC. County and Physician Variation in Benzodiazepine Prescribing to Medicare Beneficiaries by Primary Care Physicians in the USA. J Gen Intern Med. 2018 Dec;33(12):2180-2188. doi: 10.1007/s11606-018-4670-9. Epub 2018 Sep 24.
3 Li Y, Mölter A, White A, Welfare W, Palin V, Belmonte M, Ashcroft DM, Sperrin M, van Staa TP. Relationship between prescribing of antibiotics and other medicines in primary care: a cross-sectional study. Br J Gen Pract. 2019 Jan;69(678):e42-e51. doi: 10.3399/bjgp18X700457. Epub 2018 Dec 17.
4 Huygen FJ, Mokkink HG, Smits AJ, van Son JA, Meyboom WA, van Eyk JT. Relationship between the working styles of general practitioners and the health status of their patients. Br J Gen Pract. 1992 Apr;42(357):141-4.
5 Stano M. Evaluating the policy role of the small area variations and physician practice style hypotheses. Health Policy. 1993 Apr;24(1):9-17.
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