Outcomes of a tele-intensive care unit pharmacist on the management of diabetic ketoacidosis.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE:  The tele-intensive care unit (tele-ICU) pharmacist facilitates patient-specific diabetic ketoacidosis (DKA) treatment utilizing guideline-directed therapy. This study was designed to determine how patient-specific interventions by a tele-ICU pharmacist affected patients with DKA compared to the standard of care. METHODS: This retrospective cohort study utilized custom reports and manual chart review to evaluate the electronic health records of patients 18 years or older who received continuous intravenous insulin and were admitted for DKA between January 2019 and December 2020. The primary endpoint was time to DKA resolution, defined by the patient meeting at least 2 of the following criteria: a serum bicarbonate concentration of at least 18 mEq/L, an arterial pH of greater than 7.3, and closure of the anion gap (less than or equal to 12 mEq/L). RESULTS: Patients treated with tele-ICU pharmacist patient-specific interventions reached DKA resolution 7.32 hours earlier than patients treated with the standard of care (22.16 vs 29.48 hours; P = 0.0019). There was no statistically significant difference between the groups for ICU length of stay, time until subcutaneous insulin administration, incidence of hypoglycemia, incidence of severe hypoglycemia, and sodium bicarbonate use. In patients who received a tele-ICU pharmacist intervention, there was a statistically significant increase in the volume for fluid resuscitation and the amount of total continuous insulin infused after ICU admission and a statistically significant reduction in the time between laboratory draws. CONCLUSION: Treatment of patients with tele-ICU pharmacist patient-specific interventions was associated with faster DKA resolution, more frequent laboratory monitoring, and a higher volume of insulin and fluids infused than in patients treated with protocol-driven therapy.

Secular trends in gabapentinoid dispensing by compensated workers with low back pain: a retrospective cohort study.

OBJECTIVES: The increase in gabapentinoid prescribing is paralleling the increase in serious harms. To describe the low back pain workers compensation population whose management included a gabapentinoid between 2010 and 2017, and determine secular trends in, and factors associated with gabapentinoid use. METHODS: We analysed claim-level and service-level data from the Victorian workers' compensation programme between 1 January 2010 and 31 December 2017 for workers with an accepted claim for a low back pain injury and who had programme-funded gabapentinoid dispensing. Secular trends were calculated as a proportion of gabapentinoid dispensings per year. Poisson, negative binomial and Cox hazards models were used to examine changes over time in incidence and time to first dispensing. RESULTS: Of the 17 689 low back pain claimants, one in seven (14.7%) were dispensed at least one gabapentinoid during the first 2 years (n=2608). The proportion of workers who were dispensed a gabapentinoid significantly increased over time (7.9% in 2010 to 18.7% in 2017), despite a reduction in the number of claimants dispensed pain-related medicines. Gabapentinoid dispensing was significantly associated with an opioid analgesic or anti-depressant dispensing claim, but not claimant-level characteristics. The time to first gabapentinoid dispensing significantly decreased over time from 311.9 days (SD 200.7) in 2010 to 148.2 days (SD 183.1) in 2017. CONCLUSIONS: The proportion of claimants dispensed a gabapentinoid more than doubled in the period 2010-2017; and the time to first dispensing halved during this period.

Cultural, Linguistic, and Geographical Diversity of Participants in Australian Physical Activity Research Studies: A Systematic Review.

BACKGROUND: The Australian population is highly diverse in terms of cultural heritage, languages spoken, and geographical dispersion. Health outcomes are often worse among these culturally, linguistically, and geographically diverse populations, and this is reflected in rates of physical activity participation, with people from these groups often engaging in insufficient physical activity for health benefits. This research aimed to investigate if physical activity intervention studies conducted in Australia were (1) designed to recruit culturally, linguistically, and geographically diverse participants and (2) recruiting culturally, linguistically, and geographically diverse participants. METHODS: We conducted a systematic review of physical activity intervention studies conducted in adults in Australia between 2015 and November 2022. Information relevant to inclusivity in study recruitment methods and diversity of recruited participants was extracted. RESULTS: We identified and extracted data from 371 studies, of which 98 were protocol papers for which no follow-up data was available. Only 26 studies (7%) included methods to recruit culturally or linguistically diverse participants. Most studies (189, 51%) recruited participants from major city locations, with few studies recruiting from more remote locations. No studies included recruitment from very remote regions. Information on cultural, linguistic, or geographic diversity of participants recruited to physical activity studies was provided by 109 studies (40% of studies including results) with the majority recruiting White, English-speakers from major cities. CONCLUSIONS: Few Australian physical activity studies are designed to recruit culturally, linguistically, and geographically diverse participants. Due to limited reporting of the diversity of participants, comparisons with population-representative data are unreliable.

A content analysis of the interventions for low back pain promoted on the websites of Australian pain clinics.

BACKGROUND: The Internet is a widely used source of health information, yet the accuracy of online information can be low. This is the case for low back pain (LBP), where much of the information about LBP treatment is poor. METHODS: This research conducted a content analysis to explore what pain treatments for LBP are presented to the public on websites of Australian pain clinics listed in the PainAustralia National Pain Services Directory. Websites providing information relevant to the treatment of LBP were included. Details of the treatments for LBP offered by each pain service were extracted. RESULTS: In total, 173 pain services were included, with these services linking to 100 unique websites. Services were predominantly under private ownership and located in urban areas, with limited services in non-urban locations. Websites provided detail on a median of six (IQR 3-8) treatments, with detail on a higher number of treatments provided by services in the private sector. Physical, psychological and educational treatments were offered by the majority of pain services, whereas surgical and workplace-focused treatments were offered by relatively few services. Most services provided details on multidisciplinary care; however, interdisciplinary, coordinated care characterised by case-conferencing was infrequently mentioned. CONCLUSIONS: Most websites provided details on treatments that were largely in-line with recommended care for LBP, but some were not, especially in private clinics. However, whether the information provided online is a true reflection of the services offered in clinics remains to be investigated.

New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology.

Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.

Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study.

BACKGROUND: Reproductive health conditions such as endometriosis, uterine fibroids, and polycystic ovary syndrome (PCOS) affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5% to 40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased health care costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions. OBJECTIVE: This study aimed to evaluate the agreement between clinicians and 3 symptom checkers (developed by Flo Health UK Limited) in assessing symptoms of endometriosis, uterine fibroids, and PCOS using vignettes. We also aimed to present a robust example of vignette case creation, review, and classification in the context of predeployment testing and validation of digital health symptom checker tools. METHODS: Independent general practitioners were recruited to create clinical case vignettes of simulated users for the purpose of testing each condition symptom checker; vignettes created for each condition contained a mixture of condition-positive and condition-negative outcomes. A second panel of general practitioners then reviewed, approved, and modified (if necessary) each vignette. A third group of general practitioners reviewed each vignette case and designated a final classification. Vignettes were then entered into the symptom checkers by a fourth, different group of general practitioners. The outcomes of each symptom checker were then compared with the final classification of each vignette to produce accuracy metrics including percent agreement, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: A total of 24 cases were created per condition. Overall, exact matches between the vignette general practitioner classification and the symptom checker outcome were 83% (n=20) for endometriosis, 83% (n=20) for uterine fibroids, and 88% (n=21) for PCOS. For each symptom checker, sensitivity was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, and 100% for PCOS; specificity was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 75% for PCOS; positive predictive value was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, 80% for PCOS; and negative predictive value was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 100% for PCOS. CONCLUSIONS: The single-condition symptom checkers have high levels of agreement with general practitioner classification for endometriosis, uterine fibroids, and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and health care providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

Holistic Human-Serving Digitization of Health Care Needs Integrated Automated System-Level Assessment Tools.

Digital health tools, platforms, and artificial intelligence- or machine learning-based clinical decision support systems are increasingly part of health delivery approaches, with an ever-greater degree of system interaction. Critical to the successful deployment of these tools is their functional integration into existing clinical routines and workflows. This depends on system interoperability and on intuitive and safe user interface design. The importance of minimizing emergent workflow stress through human factors research and purposeful design for integration cannot be overstated. Usability of tools in practice is as important as algorithm quality. Regulatory and health technology assessment frameworks recognize the importance of these factors to a certain extent, but their focus remains mainly on the individual product rather than on emergent system and workflow effects. The measurement of performance and user experience has so far been performed in ad hoc, nonstandardized ways by individual actors using their own evaluation approaches. We propose that a standard framework for system-level and holistic evaluation could be built into interacting digital systems to enable systematic and standardized system-wide, multiproduct, postmarket surveillance and technology assessment. Such a system could be made available to developers through regulatory or assessment bodies as an application programming interface and could be a requirement for digital tool certification, just as interoperability is. This would enable health systems and tool developers to collect system-level data directly from real device use cases, enabling the controlled and safe delivery of systematic quality assessment or improvement studies suitable for the complexity and interconnectedness of clinical workflows using developing digital health technologies.

Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives.

INTRODUCTION: Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. AREAS COVERED: AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. EXPERT OPINION: The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.

Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies.

Artificial intelligence (AI) and machine learning medical tools have the potential to be transformative in care delivery; however, this change will only be realized if accompanied by effective governance that ensures patient safety and public trust. Recent digital health initiatives have called for tighter governance of digital health. A correct balance must be found between ensuring product safety and performance while also enabling the innovation needed to deliver better approaches for patients and affordable efficient health care for society. This requires innovative, fit-for-purpose approaches to regulation. Digital health technologies, particularly AI-based tools, pose specific challenges to the development and implementation of functional regulation. The approaches of regulatory science and "better regulation" have a critical role in developing and evaluating solutions to these problems and ensuring effective implementation. We describe the divergent approaches of the European Union and the United States in the implementation of new regulatory approaches in digital health, and we consider the United Kingdom as a third example, which is in a unique position of developing a new post-Brexit regulatory framework.