Optimising the secondary use of primary care prescribing data to improve quality of care: a qualitative analysis

Abstract

ObjectivesTo explore available data sources, secondary uses and key considerations for optimising the actionability of primary care prescribing data to improve quality of care in the Dutch context.DesignAn exploratory qualitative study was undertaken based on semi-structured interviews. We anchored our investigation around three tracer prescription types: antibiotics; benzodiazepines and opioids. Descriptive and explanatory themes were derived from interview data using thematic analysis.SettingStakeholders were sampled from across the micro (clinical), meso (organisational) and macro (policy) contexts of the Dutch primary care system.ParticipantsThe study involved 28 informants representing general practitioners (GPs), community pharmacists, regional chronic care networks (care groups), academia and research institutes, insurers, professional associations, electronic health record (EHR) vendors and national authorities.ResultsIn the Netherlands, three main sources of data for improving prescribing in primary care are in use: clinical data in the EHRs of GP practices; pharmacy data in community pharmacy databases and claims data of insurers. While the secondary use of pharmacy and claims data is well-established across levels, the use of these data together with EHR data is limited. Important differences in the types of prescribing information needed by micro-meso-macro context are found, though the extent to which current indicators address these varies by prescription type. Five main themes were identified as areas for optimising data use: (1) measuring what matters, (2) increasing data linkages, (3) improving data quality, (4) facilitating data sharing and (5) optimising fit for use analysis.ConclusionsTo make primary care prescribing data useful for improving quality, consolidated patient-specific data on the indication for a prescription and dispensed medicine, over time, is needed. In the Netherlands, the selection of indicators requires further prioritisation to better signal the appropriateness and long-term use of prescription drugs. Prioritising data linkages is critical towards more actionable use.

Development of prescribing indicators related to opioid-related harm in patients with chronic pain in primary care—a modified e-Delphi study

“…However, it does not identify individual patients affected by opioid prescribing within clinical prescribing systems. Although these indicators exist, they do not fully encompass individual settings’ unique characteristics and challenges and the contexts of polypharmacy [ 28 ]. Additionally, while local and national prescribing guidance and recommendations are available, there is a lack of consensus on best practices [ 29 ].…”

The impact of data extraction and processing on outcomes of research based on routine healthcare data from general practices: an observational study (Preprint)

“…[1][2][3][4] These EHR data are frequently used to report the incidence, prevalence, and the use of health services [3,[5][6][7][8], as well as for applications in practice such as decision support, and monitoring and feedback for healthcare providers to improve the quality of care. [9,10] In the Netherlands, general practice EHR data plays an important role in research and policy making. For example, during the COVID-19 pandemic, EHR data were used to monitor the spread of the disease and the use of health services.…”

TNFα inhibitor treatment trajectories

“…The retransitioning and biosimilar remainder cohorts had some differences in baseline characteristics; patients in the retransitioning cohort were younger (median 39.9 years versus 44.0 years in the biosimilar remainder cohort), were more often female (65.9% versus 48.9%), and had a shorter median dosing interval than patients in the biosimilar remainder cohort (48.5 days [IQR 42-56] days vs 56 days [IQR [45][46][47][48][49][50][51][52][53][54][55][56]), as depicted in Table 1. The main reasons for patients to retransition were loss of effect (36.4%), adverse events (29.5%) or both loss of effect and adverse events (22.7%) (Figure 2).…”

“…In the studies that reported on gainsharing, the financial savings from transitioning to the biosimilar were used for a "long-term appointment of a switch pharmacist" 47 (not further specified) and for the appointment of IBD nurses and pharmacists for a "nurse-led IBD biologicals service for improving IBD patient safety and quality of care". 48 Patients might be more positive about biosimilars when they directly benefit from the financial savings, but the number of studies was too limited for final conclusions. Providing patients with the option of retransitioning at the introduction of the biosimilar-if the patient is not satisfied with the biosimilar-increased the incidence of retransitioning.…”

“…47 However, they still need training to improve the quality of their coding. 48 This should be embedded in clinicians' prescription training and facilitated by the EHR.…”