Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process

<p>Abstract</p> <p>Background</p> <p>Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines.</p> <p>Methods</p> <p>Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia.</p> <p>Results</p> <p>A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases.</p> <p>Conclusions</p> <p>This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries.</p

Developing Safe Medication Practices within a Regional Health Care District in Finland

The thesis had the following three objectives: I. To develop and validate a medication safety self-assessment tool (MSSA) in secondary care hospital wards (Study I: organizational level); II. To use clinical pharmacist-conducted collaborative medication reviews (CMRs) in an emergency department (ED) short-term ward to identify inappropriate prescribing (IP) in pre-admission medications; (Study II: health care unit and clinical practice level); III. To investigate how well older people are aware of the major potential risks of benzodiazepines and related drugs (BZD) they are taking and whether the risk awareness changed between 2004 and 2015 (Study III: patient care and medication use level). In Study I (2008-2011), the original MSSA tool (231 items under ten components) was first modified preliminarily and then by the Delphi expert panel (14 panelists) with four rounds. The modified MSSA tool was then pilot tested on 8 hospital wards of various specialties in a regional secondary care hospital. Several safety recommendations were documented, including the development of clinical pharmacy services. In Study II (2016), pre-admission medications of patients were reviewed by the pharmacist. BZD (29%) and antidepressants (28%) were involved in over half of the confirmed IP events. In Study III (2004 and 2015), patients were personally interviewed to determine how well they were aware of the potential risks of the BZD they were taking and whether the risk awareness had changed in the years between the two study periods. The study found that awareness of dependence (p=0.047), interaction with alcohol (p=0.001), dizziness (p=0.002), and developing tolerance (p=0.002) had improved, while awareness of the other potential risks remained unchanged. This thesis found that the modified and validated MSSA tool can be used to support building up safe medication practices in health care organizations, particularly establishing ward-based pharmacotherapy plans (Study I). The pharmacist-led CMR practice was found helpful in ED admissions for older residents (>65 years) in ED admissions (Study II). Older BZD users’ awareness of potential risks related to BZD use was improved between 2004 and 2015. Despite improved patient awareness, no significant change was found in their willingness to discontinue BZD therapy (Study III). National-level coordination is needed to integrate the modified MSSA tool for hospitals as a part of national patient safety policies in Finland (Study I). More research is required to assess whether CMR practice in the ED could impact preventable ED re-admissions (Study II). Future research should also investigate patients’ risk awareness of different high-risk medications, especially in older users (Study III).Tämän väitöskirjan tavoitteina oli I. Kehittää ja validoida lääkitysturvallisuuden itsearviointityökalu hoitoyksiköiden käyttöön keskussairaalassa (organisaatiotaso); II. Muodostaa moniammatilliseen yhteistyöhön perustuva lääkehoidon arviointikäytäntö keskussairaalan päivystysosastolle epäsopivan kotilääkityksen tunnistamiseksi (hoitoyksikkötaso); III. Haastatella kaupunginsairaalan vuodeosastoilla iäkkäitä potilaita heidän tietämyksestään unilääkkeinä käyttämiensä bentsodiatsepiinien potentiaalisista haittavaikutuksista (potilastaso). Ensimmäisessä osatyössä (2008-2011) sovellettiin yhdysvaltalaisen Institute for Safe Medication Practices organisaation alkuperäistä mittaristoa: the Medication Safety Assessment Tool for Hospitals suomalaiseen sairaalaympäristöön käyttäen Delphi-konsensusmenetelmää (neljä asiantuntijakierrosta). Työkalua pilotoitiin eri vuodeosastoilla ja tuloksena tunnistettiin lääkitysturvallisuuden näkökulmasta kehitettäviä asioita, joista yhtenä osa-alueena nousi esille kliinisen farmasiatoiminnan kehittäminen. Toisessa osatyössä (2016) arvioitiin päivystykseen tulleiden potilaiden lääkityksiä. Epäsopiviksi lääkemääräyksiksi tunnistettuja yleisempiä lääkeaineryhmiä olivat bentsodiatsepiinit (29 %) ja antidepressantit (28 %), jotka yhdessä muodostivat yli puolet kaikista havainnoista. Kolmannessa osatyössä (2004 ja 2015) tutkittiin bentsodiatsepiinia unilääkkeenä käyttäneiden iäkkäiden tietämystä näiden lääkkeiden potentiaalisista haitoista haastattelemalla potilaita. Potilaiden haittavaikutustietämyksessä havaittiin parannusta seuraavissa asioissa: riippuvuus (p=0.047), vaikutuksen voimistuminen alkoholin kanssa (p=0.001), huimaus (p=0.002) ja toleranssin kehittyminen (p=0.002). Muiden potentiaalisten haittojen kohdalla muutosta ei havaittu. Lääkitysturvallisuuden itsearviointityökalu osoittautui olevan hyvin linjassa kansallisten lääkitysturvallisuussuositusten kanssa ja tukevan suositusten toteuttamista käytännössä. Itsearviointityökalu nähtiin hyödyllisenä erityisesti lääkehoitosuunnitelmien laatimisessa ja lääkehoitosuunnitelmien sisällön kehittämisessä organisaatiotasolla. Lääkityksen moniammatilliset arvioinnit osoittautuivat hyödyllisiksi tunnistamaan sairaalan päivystysosaston potilaiden kotilääkityksissä esiintyviä riskejä ja ongelmia. Parannusta havaittiin potilaiden tietämyksissä joistakin bentsodiatsepiinien haitoista. Tästä huolimatta tutkimuksessa ei havaittu muutosta potilaiden halukkuudessa lopettaa bentsodiatsepiinilääkitys. Sairaaloille tarkoitettu lääkitysturvallisuuden itsearviointityökalu soveltuu hyödynnettäväksi kansallisissa potilasturvallisuuslinjauksissa. Sairaaloiden hoitoyksiköissä potilaiden lääkityksen moniammatilliset arvioinnit soveltuvat rutiinikäytäntöihin. Iäkkäiden potilaiden tietämystä heillä käytössä olevien riskilääkkeiden haitoista tulisi tutkia laajemmin, jotta potilaita voitaisiin motivoida esim. lääkitysten lopettamiseen tai turvallisempien hoitovaihtoehtojen käyttämiseen

Patient safety : Cardio and cerebrovascular risk of major adverse events following exposure to potentially inappropriate medications

Chapter 1 introduces what is known about the current challenges of medication management in the elderly, with a special focus on management of potentially inappropriate medications (PIMs) in clinical practice. Available evidence on PIMs with cardiac and cerebrovascular risk of adverse events, especially in terms of major outcomes (Major Adverse Cardiac and Cerebrovascular risk of Adverse Events – MACCE), is described. To answer to gaps in the knowledge identified during the literature review, a systematic review was conducted, described in Chapter 2.1, which showed that PIM‐lists focus mainly on common adverse events and often poorly describe the potential consequence for MACCE occurrence. To evaluate the extent of utilisation of such medications in older individuals, we conducted a prevalence study in ambulatory care and in longterm care facilities, described in Chapter 2.2, where we found that 59.4% patients were taking medications with Cardiac and Cerebrovascular Adverse Events (CCVAEs) risk, including 38.8% who used drugs with MACCE risk. Fifty percent of patients with a previous history of cardiovascular diseases were taking PIMs with risk of CCVAEs, including 30.0% with risk of MACCE. We also found a high proportion of patients using antipsychotics (APs), described as PIMs in the literature. In order to establish the mechanisms that may be linked to the occurrence of these events when using APs, we conducted a case/non-case study in a global pharmacovigilance database (Chapter 3.1). We found that APs with high affinity for Adrenergic alfa-1, Histaminic H1, Muscarinic M1, and Serotoninergic 5-HT2A receptors and with high-risk of metabolic side effects profile may explain the occurrence of those events. In Chapter 4.1 and 4.2, we have explored the knowledge of healthcare professionals (HCPs, including physicians, pharmacists, and nurses) on medication complexities among the elderly population, and the barriers experienced in managing these, particularly in managing PIMs. In this chapter, we also explored the patient-related features (PRFs) that should be considered when initiating treatment with APs in older individuals with dementia, and aspects to be focused during treatment monitoring. In Chapter 4.1, we found that most HCPs felt confident to manage medication complexities in elderly patients with dementia, but only a minority obtained a good score in the knowledge assessment test. The main barriers identified included structural barriers (tools unfit for practice) and process barriers (time), suggesting education per se will not necessarily lead to optimised pharmacotherapy in the elderly. Moreover, it seems that new tools, like clinical decision support systems (CDSS), are needed to facilitate the work of HCPs in daily practice, helping them to stratify the risk of adverse drug reactions (ADRs) when prescribing specific drugs. In Chapter 4.2, we found that, even though a high number of PRFs were rated as clinically relevant, some of them were identified as frequently missing from electronic medical records. Chapter 5 discusses all the results from the previous chapters. Overall, the conducted research shows person-centred tools are needed to consider the heterogeneity inside this population subgroup, as the ones available nowadays are more focused on the medication itself or even on different subgroups defined by comorbidities. Moreover, for such tools to be implemented in clinical practice, they need to be embedded into the software system and resort to data linkage, so that the full potential of electronic records is gauged. To consider a more tailored approach, a stratification risk calculator along within an electronic decision-making support would be of great interest to foster safe prescribing of medications in the elderly, particularly among those with dementia

Investigating the prevalence and causes of prescribing errors in general practice : the PRACtICe Study

Report prepared by the University of Nottingham, University of Reading and University of Hertfordshir

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background: Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed. Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully. Methods: We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist. Results: We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy. Conclusions: The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed

L'usage secondaire des données médico-administratives afin d’optimiser l’usage des médicaments chez les patients atteints de maladies respiratoires chroniques : adhésion aux médicaments, identification de cas et intensification du traitement

Medication adherence in patients with asthma and chronic obstructive pulmonary disease (COPD) is notoriously low and is associated with suboptimal therapeutic outcomes. To intervene effectively, family physicians need to assess medication adherence efficiently and accurately. Otherwise, failure to detect nonadherence may further reduce patient disease control and result in unnecessary treatment escalation that can increase the risk of adverse events and lead to more complex and costly drug regimens. The overarching goal of this thesis was to investigate how the use of secondary healthcare data can be leveraged to optimize medication adherence in clinical practice. Methodological considerations to facilitate our understanding of treatment escalation in asthma using secondary healthcare data were also examined. In the first part of my doctoral research program, I led a project which aimed at developing e-MEDRESP, a novel web-based tool built from pharmacy claims data that provides to family physicians with objective and easily interpretable information on patient adherence to asthma/COPD medications. This tool was developed in collaboration with family physicians and patients using a framework inspired by user-centered design principles. As part of a feasibility study, e-MEDRESP was subsequently implemented in electronic medical records across several family medicine clinics in Quebec (346 patients, 19 physicians). Findings showed that its integration within physician workflow was feasible. Physicians reported that the tool helped to: 1) better evaluate their patients’ medication adherence; and 2) adjust prescribed therapies, with mean ± sd ratings (5-point Likert scale) of 4.8±0.7 and 4.3±0.9, respectively. A pre-post analysis did not reveal improvement in adherence among patients whose physician consulted e-MEDRESP during a medical visit. However, significant improvements in adherence for inhaled corticosteroids (Proportion of days covered (PDC): 26.4% (95% CI: 14.3-39.3%)) and long-acting muscarinic agents (PDC: 26.4% (95% CI: 12.4-40.2%)) were observed among patients whose adherence level was less than 80% in the 6-month period prior to the medical visit. The second part of this research program consisted of two studies which laid the groundwork to estimate the association between medication adherence and treatment escalation in asthma using Canadian healthcare administrative data, a phenomenon that is currently under-explored in the literature. Prior to embarking in this study, it is important to ensure that healthcare administrative databases can be used to identify asthma patients and treatment escalations in an adequate manner. First, a systematic review was conducted to obtain an overview of the available evidence supporting the validity of algorithms to identify asthma patients in healthcare administrative databases. The algorithm developed by Gershon et al. (Canadian Respiratory Journal, 2009;16(6):183-188) comprising ≥2 ambulatory medical visits or ≥1 hospitalization for asthma over two years had the best trade-off between sensitivity (84 %) and specificity (77%). Second, an operational definition of treatment escalation was developed through a Delphi study that incorporated an expert consensus process. This definition includes 7 steps and was inspired by the 2020 Global for Initiative for Asthma treatment guidelines. I plan to integrate the definitions obtained from these two studies in a future cohort study which aims to examine the association between medication adherence and treatment escalation in asthma. My research provides compelling evidence on the importance of developing and evaluating the feasibility of implementing tools which can aid physicians in assessing medication adherence in clinical practice and extends the literature on treatment escalation in asthma.L’adhésion aux médicaments chez les patients présentant un asthme ou une maladie pulmonaire obstructive chronique (MPOC) est reconnue pour être faible. Pour intervenir efficacement, les médecins de famille doivent évaluer de manière précise l’adhésion aux médicaments. Ne pas détecter la non-adhésion peut réduire davantage la maîtrise de la maladie, entraîner une intensification non-nécessaire du traitement, mener à des schémas pharmacologiques plus complexes et coûteux et par conséquent, augmenter le risque d’événements indésirables. La présente thèse vise à approfondir les connaissances sur l'usage secondaire des données médico-administratives afin d’optimiser l’adhésion et l’usage des médicaments chez les patients atteints de maladies respiratoires chronique, au moyen d’une approche méthodologique mixte de recherche. Plusieurs questions méthodologiques cruciales concernant l’étude de l’intensification du traitement en asthme ont également été abordées. Le premier axe porte sur le développement de l’outil e-MEDRESP, qui s’appuie sur les renouvellements d’ordonnances et qui est conçu pour donner rapidement accès aux médecins de famille à une mesure objective et facilement interprétable de l’adhésion aux médicaments utilisés dans le traitement de l’asthme et de la MPOC. L’outil a été développé en collaboration avec des médecins de famille et des patients à l’aide de groupes de discussion et d’entrevues individuelles. Dans le cadre d’une étude de faisabilité, l’outil e-MEDRESP a été par la suite implanté dans les dossiers médicaux électroniques de plusieurs cliniques de médecine familiale au Québec (346 patients, 19 médecins). Les résultats ont montré que l’intégration de d’e-MEDRESP dans le flux de travail des médecins était faisable. Les médecins ont indiqué que l’outil leur a permis de : 1) mieux évaluer l’adhésion aux médicaments de leurs patients (cote moyenne et écart-type sur une échelle de Likert à 5 points [perception d’accord] de 4,8±0,7); et 2) ajuster les traitements prescrits (4,8±0,7 et 4.3±0,9). Une analyse pré-post n’a pas révélé d’amélioration au niveau de l’adhésion aux médicaments chez les patients dont le médecin a consulté e-MEDRESP lors d’une visite médicale. Toutefois, une amélioration statistiquement significative a été observée chez les patients dont le niveau d’adhésion était inférieur à 80 % au cours de la période de six mois précédant la visite et qui étaient traités par des corticostéroïdes inhalés (Proportion of days covered (PDC) = 26,4 % (IC à 95 % : 14,3-39,3 %) ou des antagonistes muscariniques à action prolongée (PDC = 26,9 % (IC à 95 % : 12,4-40,2 %)). Le deuxième axe présente des travaux préparatoires à la conduite d’une cohorte qui sera réalisée à partir de bases de données médico-administratives et qui aura comme objectif d’estimer l’association entre l’adhésion aux médicaments et l’intensification du traitement de l’asthme, une question peu explorée à ce jour. Avant de débuter une telle étude, il est important de s’assurer que les bases de données médico-administratives peuvent être utilisées pour identifier de manière adéquate les patients asthmatiques et l’intensification du traitement. Dans un premier temps, une revue systématique a été effectuée pour identifier les données probantes disponibles concernant la validité des algorithmes permettant d’identifier les patients asthmatiques dans les bases de données médico-administratives. L’algorithme qui a été développé par Gershon et coll. (Revue canadienne de pneumologie, 2009; vol. 16, no 6, p. 183-188), qui comprenait deux visites médicales ambulatoires ou une hospitalisation pour asthme sur deux ans, présentait le meilleur compromis entre la sensibilité (84 %) et la spécificité (77 %). Dans un second temps, une définition opérationnelle de l’intensification du traitement a été élaborée dans le cadre d’une étude Delphi qui incorporait un processus consensuel d’experts. Cette définition comprend sept étapes et s’inspire des lignes directrices 2020 de l'initiative mondiale de lutte contre l'asthme. Les définitions obtenues à partir de ces deux études seront intégrées dans l’étude de cohorte. Les études constituant cette thèse démontrent l’importance de développer des outils qui permettent aux médecins d’évaluer l’adhésion aux médicaments dans leur pratique clinique, en plus d’enrichir la littérature scientifique médicale sur l’intensification du traitement chez les patients asthmatiques

Early development of decision support systems based on artificial intelligence: an application to postoperative complications and a cross-specialty reporting guideline for early-stage clinical evaluation

Background: Complications after major surgery occur in a similar manner internationally but the success of response process in preventing death varies widely depending on speed and appropriateness. Artificial intelligence (AI) offers new opportunities to provide support to the decision making of clinicians in this stressful situation when uncertainty is high. However, few AI systems have been robustly and successfully tested in real-world clinical settings. Whilst preparing to develop an AI decision support algorithm and planning to evaluate it in real-world settings, a lack of appropriate guidance on reporting early clinical evaluation of such systems was identified. Objectives: The objectives of this work were twofold: i) to develop a prototype of AI system to improve the management of postoperative complications; and ii) to understand expert consensus on reporting standards for early-stage evaluation of AI systems in live clinical settings. Methods: I conducted and thematically analysed interviews with clinicians to identify their main challenges and support needs when managing postoperative complications. I then systematically reviewed the literature on the impact of AI-based decision support systems on clinicians’ diagnostic performance. A model based on unsupervised clustering and providing prescription recommendations was developed, optimised, and tested on an internal hold out dataset. Finally, I conducted a Delphi process, to reach expert consensus on minimum reporting standards for the early-stage clinical evaluation of AI systems in live clinical settings. Results: 12 interviews were conducted with junior and senior clinicians identifying 54 themes about challenges, common errors, strategies, and support needs when managing postoperative complications. 37 studies were included in the systematic review, which found no robust evidence of a positive association between the use of AI decision support systems and improved clinician diagnostic performance. The developed algorithm showed no improvement in recall at position ten compared to a list of the most common prescriptions in the study population. When considering the prevalence of the individual prescriptions, the algorithm showed a 12% relative increase in performance compared to the same baseline. 151 experts participated in the Delphi study, representing 18 countries and 20 stakeholder groups. The final DECIDE-AI checklist comprises 27 items, accompanied by Explanation & Elaboration sections for each. Conclusion: The proposed algorithm offers a proof of concept for an AI system to improve the management of postoperative complications. However, it needs further development and evaluation before claiming clinical utility. The DECIDE-AI guideline provides a practicable checklist for researchers reporting on the implementation of AI decision support systems in clinical settings, and merits future iterative evaluation-update cycles in practice