Pharmaceutical Services

Three different algorithms were used to identify patients with RA by using the Medicare claim data from 1994 to 2004: (a) beneficiaries with at least two claims associated with RA (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9 CM] code 714), (b) beneficiaries with at least three claims associated with RA, and (c) beneficiaries with at least two RA claims that were from a rheumatologist and that were separated by at least 7 days. All inpatient, outpatient, and procedure claims such as laboratory or radiologic tests were included. We identified rheumatologists with a Medicare provider specialty code in the database and verified them with the ACR membership directory. A subgroup of patients who filled at least one prescription for DMARDs over a period of 1 year after the RA diagnosis was then identified by using the data from both pharmacy benefit program and claim data for infusions. To compare baseline characteristics of the study subjects, we selected a group of beneficiaries who never had any claims for RA.
After identifying subjects by each of the algorithms, we attempted to obtain consent to review their medical record. First, the PACE program mailed a letter to the groups of subjects identified by our algorithms to inform them that they would be contacted by our research group. A letter that provided details about the study was then sent to the subjects in each of the groups and asked whether they would consent to have the study researchers review their medical records from their physicians, including doctors who treated them for arthritis. Subjects who agreed to participate in the study signed a consent and authorization form for release of medical records. Additionally, subjects were asked to complete a physician information form to identify their primary physicians as well as specialists and their contact information. We then attempted to obtain copies of medical records.
Once we received the medical records, all personal identifiers were removed from the records for protection of patients' privacy. Medical records were reviewed independently by several rheumatologists at Brigham and Women's Hospital. To minimize inter-reviewer variation in data abstraction, a structured data abstraction form was developed and pilot-tested with the principal investigator (DHS). The form included items such as the seven ACR 1987 classification criteria for RA, disease onset, other rheumatologic diagnoses, medications, and laboratory data. On the basis of these data, the reviewers assessed whether a patient met the gold standard definitions of RA: (a) diagnosis of RA by a rheumatologist and (b) fulfillment of the ACR criteria for RA. Any indication in the medical record that the diagnosing rheumatologists thought that the patient had RA at that time was counted as having 'RA diagnosis per rheumatologists'. When the patients were not seen by rheumatologists, 'RA diagnosis per rheumatologists' was made by the reviewers on the basis of the data from their medical records. When the diagnosis of RA was neither documented nor clear in their medical records, the patients were considered non-RA. Areas of disagreement or uncertainty were resolved by consensus. The study period for data collection from medical records lasted from 2004 to 2008.

Before randomization, comprehensive medication reviews were conducted by BCGPs for all participants using participant-reported medical conditions and information on dose, frequency, indication, duration of treatment, tolerability, and adverse drug reactions for all prescription medications, vitamins, and supplements. The BCGP medication review process involved 1) assessing the clinical appropriateness of each medication using the Beers Criteria [13 ] and Medication Appropriateness Index (MAI); [16 (link)] 2) evaluating potential drug-drug and drug-disease interactions in accord with the above and also taking into account prescription label information; and 3) assessing whether medication regimens followed relevant disease-specific evidence-based guidelines [13 , 17 (link), 18 (link)]. Of note, blood laboratory work results, electronic medical records, and previous therapies (e.g., medication failures) were not available to BCGPs when devising baseline recommendations, but were available to the clinician member of the MTM team. Following randomization, the MTM recommendations were only shared with those participants randomized to the intervention group (N = 46). Recommendations for the control group were recorded in the study database but not shared with those participants.
During the INCREASE study period, the pharmacy team of two BCGPs utilized drug and health information resources (e.g., Lexicomp and UpToDate [Wolters Kluwer Health Inc. Riverwoods, IL]), Beers Criteria [13 ], relevant guidelines (e.g., Diabetes Standards of Care [17 (link)] and Clinical Practice Guidelines for Hypertension [18 (link)]), and clinical judgement to justify their recommendations. Each recommendation was reviewed by both BCGPs and a consensus pharmacy recommendation was decided via discussion. Detailed information for each recommendation was then entered into a series of pre-specified study protocol data collection forms, allowing for systematic categorization of recommendations as either: 1) medication discontinuation with or without tapering; 2) switch to a different medication; 3) dose adjustment (e.g., decrease dose, adjust dose for organ function/tolerability, or increase dose); 4) new medication initiation; 5) drug or disease monitoring recommendation (e.g., vital signs, falls risk, sedation); or 6) a non-pharmacologic recommendation (e.g., sleep hygiene, avoiding gastroesophageal reflux triggers, referral for diagnostic workup). Baseline recommendations were also categorized by pharmacologic class and over the counter (OTC) or supplement status of the medication prompting a baseline MTM recommendation. A full schematic for medication categorization is available in the supplementary material (see Supplementary Table S1).

This paper draws on interview-based research conducted from May to June 2022, as part of a broader project investigating how information about medicines moves between patients, pharmacists, and general practitioners. To explore how GP’s approached prescribing decisions and understood their relationships with patients and pharmacists around medicines, we elected to use an interview methodology. Interview methodologies are well-suited to research that investigates people’s thoughts and experiences, and the semi-structured format accommodates emergent findings that might not be anticipated in the original research design. The project received ethics approval from the Victoria University of Wellington Human Ethics Committee (#28324).
Given our aim to solicit general practitioners’ views specifically, we followed a purposive sampling strategy and recruited from a national pool, so as to gain cross-sectional insights from different geographic, socioeconomic, and institutional settings. Using the Medidata database of general practitioners, we issued an invitation for interview participants that reached 1,331 recipients. Of these, 25 people registered their interest via the supplied link, and 16 followed through to an interview. Our only selection criterion was that participants must be currently practicing general practitioners, as all 25 initial respondents were. Recruitment stopped when participants stopped opting in to the study.
While most of our resulting sample worked full-time in general practice, some of our interviewees worked part-time, as locums, or combined their general practice work with, e.g., working for hospice. Of our sixteen participants, 10 were female and six male. Eight were based in one of New Zealand’s three major cities, six in a smaller city or large town, and two rurally. Thus, while our sample is not representative, it does encompass a range of professional and regional experiences and clear themes were evident across the data set.
Interview guides were developed from the overall project’s aims, and included questions about participants’ professional backgrounds and contexts, prescribing practices and views on medicines, relationships to pharmacists, and perspectives on the New Zealand health system. In keeping with the semi-structured interview process, we allowed these guides to steer our conversations without dictating or unnecessarily limiting their course. The interview procedure was not adjusted in light of emergent findings, so as to ensure maximum comparability across the data set. However, the interview guide was adapted in some cases to suit individual participants’ time availability.
The authors conducted all interviews via Zoom, between May 1 and June 30 2022. Interviews lasted from 24 to 68 min, with a mean duration of 46 min 30 s. Typically, shorter interviews were those conducted during participants’ lunch breaks, and longer ones were conducted on participants’ days off or in the evenings. All interviews were audio recorded using the computer’s inbuilt recording software, then professionally transcribed verbatim. The original audio files and resulting transcripts were allocated pseudonymised alphanumeric file names (linked to identifying information in one securely stored spreadsheet) and stored in the University’s secure cloud storage system.
Although Denise Taylor training as a pharmacist could potentially be expected to influence how interviewees spoke about the pharmacists they interacted with, we did not note any discernible differences in the results collected by each interviewer, and neither had any existing relationship with any participants. It is possible that conducting interviews via Zoom also mitigated the extent to which the researchers’ positionality shaped interviews. We were known to participants’ primarily by our qualifications, professional roles, and association with a respected funder, with few cues as to our respective physical presentations beyond the shoulders-up Zoom window.
Our data analysis was conducted by (Courtney Addison) June through October 2023, and checked and discussed with Denise Taylor. Our analytic process was grounded in the constructivist tradition of Corbin and Strauss [34 ], which acknowledges the role of the analyst in meaning-making, and followed a Reflexive Thematic Analysis process [35 (link)]. This began with familiarisation with the data set (all interview transcripts), followed by iterative, open coding of the corpus. Codes were then reviewed and grouped into themes, which were themselves reviewed against the transcripts. The review process enabled refinements to the theme, so that, for example, the theme ‘Doctor/pharmacist interactions’ became ‘Sharing information about patients’, ‘Seeking medicines information’, ‘Correcting mistakes’. To check the validity of findings, the authors discussed transcripts and codes from their distinct disciplinary perspectives (Anthropology and Pharmacy, respectively) and also compared findings against the data set from the other arm of this project, which consists of interviews and observations from a community pharmacy. This confirmed, for example, that doctors were using pharmacists in the ways they described in the interviews reported on here, and that pharmacists were indeed picking up mistakes as interviewees report.