Adam J Hodgkins

Feasibility of implementing routine nutritional screening for older adults in Australian general practices: a mixed-methods study

BackgroundNutrition screening in older adults is not routinely performed in Australian primary care settings. Low awareness of the extent of malnutrition in this patient group, lack of training and time constraints are major barriers that practice staff face. This study aimed to demonstrate the feasibility of including a validated nutrition screening tool and accompanying nutrition resource kit for use with older patients attending general practice. Secondary aims were to assess nutrition-related knowledge of staff and to identify the extent of malnutrition in this patient group.MethodsNine general practitioners, two general practice registrars and 11 practice nurses from three participating general practices in a rural, regional and metropolitan area within a local health district of New South Wales, Australia were recruited by convenience sampling.Individual in-depth interviews, open-ended questionnaires and an 11-item knowledge questionnaire were completed three months following in-practice group workshops on the Mini Nutritional Assessment Short Form (MNA-SF). Staff were encouraged to complete the MNA-SF within the Medicare-funded 75+ Health Assessment within this time period. Staff interviews were digitally recorded, transcribed verbatim and analysed thematically using qualitative analysis software QSR NVivo 10.ResultsFour key themes were determined regarding the feasibility of performing MNA –SF: ease of use; incorporation into existing practice; benefit to patients’ health; and patients’ perception of MNA-SF. Two key themes related to the nutrition resource kit: applicability and improvement. These findings were supported by open ended questionnaire responses. Knowledge scores of staff significantly improved from baseline (52% to 66%; P < 0.05). Of the 143 patients that had been screened, 4.2% (n = 6) were classified as malnourished, 26.6% (n = 38) `at risk’ of malnutrition and 69.2% (n = 99) as well-nourished.ConclusionIt is feasible to include the MNA-SF and a nutrition resource kit within routine general practice, but further refinement of patients’ electronic clinical records in general practice software would streamline this process.

A valuable approach to the use of electronic medical data in primary care research Panning for gold

Background: Electronic medical data (EMD) from electronic health records of general practice computer systems have enormous research potential, yet many variables are unreliable. Objective: The aim of this study was to compare selected data variables from general practice EMD with a reliable, representative national dataset (Bettering the Evaluation and Care of Health (BEACH)) in order to validate their use for primary care research. Method: EMD variables were compared with encounter data from the nationally representative BEACH program using χ2 tests and robust 95% confidence intervals to test their validity (measure what they reportedly measure). The variables focused on for this study were patient age, sex, smoking status and medications prescribed at the visit. Results: The EMD sample from six general practices in the Illawarra region of New South Wales, Australia, yielded data on 196,515 patient encounters. Details of 90,553 encounters were recorded in the 2013 BEACH dataset from 924 general practitioners. No significant differences in patient age (p = 0.36) or sex (p = 0.39) were found. EMD had a lower rate of current smokers and higher average scripts per visit, but similar prescribing distribution patterns. Conclusion: Validating EMD variables offers avenues for improving primary care delivery and measuring outcomes of care to inform clinical practice and health policy.

Survival analysis using primary care electronic health record data: A systematic review of the literature

Purpose: An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. Method: An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. Results: A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005–2014) with a majority (n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half (n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. Conclusion: Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

Validation of electronic medical data: Identifying diabetes prevalence in general practice

Background: Electronic medical records are increasingly used for research with limited external validation of their data. Objective: This study investigates the validity of electronic medical data (EMD) for estimating diabetes prevalence in general practitioner (GP) patients by comparing EMD with national Bettering the Evaluation and Care of Health (BEACH) data. Method: A “decision tree” was created using inclusion/exclusion of pre-agreed variables to determine the probability of diabetes in absence of diagnostic label, including diagnoses (coded/free-text diabetes, polycystic ovarian syndrome, impaired glucose tolerance, impaired fasting glucose), diabetic annual cycle of care (DACC), glycated haemoglobin (HbA1c) > 6.5%, and prescription (metformin, other diabetes medications). Via SQL query, cases were identified in EMD of five Illawarra and Southern Practice Network practices (30,007 active patients; from 2 years to January 2015). Patient-based Supplementary Analysis of Nominated Data (SAND) sub-studies from BEACH investigating diabetes prevalence (1172 GPs; 35,162 patients; November 2012 to February 2015) were comparison data. SAND results were adjusted for number of GP encounters per year, per patient, and then age–sex standardised to match age–sex distribution of EMD patients. Cluster-adjusted 95% confidence intervals (CIs) were calculated for both datasets. Results: EMD diabetes prevalence (T1 and/or T2) was 6.5% (95% CI: 4.1–8.9). Following age–sex standardisation, SAND prevalence, not significantly different, was 6.7% (95% CI: 6.3–7.1). Extracting only coded diagnosis missed 13.0% of probable cases, subsequently identified through the presence of metformin/other diabetes medications (*without other indicator variables) (6.1%), free-text diabetes label (3.8%), HbA1c result* (1.6%), DACC* (1.3%), and diabetes medications* (0.2%). Discussion: While complex, proxy variables can improve usefulness of EMD for research. Without their consideration, EMD results should be interpreted with caution. Conclusion: Enforceable, transparent data linkages in EMRs would resolve many problems with identification of diagnoses. Ongoing data quality improvement remains essential.