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Personalized medicine, also referred to as precision medicine, deals with a clinical model that delineates patients into different groups based on their ethnicity, lifestyle, food habits, medical history, drug reaction, comorbidity, the robustness of the immune system, age, gender, and proneness to infection, overall psyche and attitude towards life. Further, emotions, social interactions and life experiences culminating into overall happiness play an important role in the life of a person. Thus, an emotionally strong and happy person is usually healthy. Taking all these above factors into consideration and with accurate diagnosis, a drug may be prescribed more in tune with the uniqueness of the patient’s genome. Since everybody, whether diseased or healthy, has a unique genome, this uniqueness must be utilized in deciding the drug, dose and its long-term effects. Healing and cure should address the root cause of the problem instead of working only on the symptoms to provide short-term relief. In addition, repurposing of the drug which is not an old concept should also be carefully explored because with this approach, a large number of already available drugs may be used for a much wider number of diseases than the medicine originally developed for. This will also help reduce the cost of the development of medicine. Finally, clinicians and doctors should be sensitized to the concept of precision medicine and its less obvious sub-disciplines. This is envisaged to provide better, more accurate diagnosis and may result in better treatment. The medical field, besides being a deep science is also an art starting from how to deal with diverse types of patients of different backgrounds and educate them all the way to instill a sense of confidence and then to prescribe the medicine to cure the disease. Seemingly, within the realm of precision medicine, it is a huge task. However, it is possible to collect and analyze diagnostic data to reach a consensus. This would require the involvement of clinical psychologists and genetic counselors in a hospital setting ensuring that patient care is holistic, taking into account both the physical and psychological aspects of health. This integrated approach can lead to improved patient care and long-term well-being.

Clinical Pharmacology Update| October 26 2023 Bexagliflozin Megan Giruzzi; Megan Giruzzi 1Washington State University, College of Pharmacy and Pharmaceutical Sciences, Spokane, WA Corresponding author: Megan Giruzzi, megan.giruzzi@wsu.edu Search for other works by this author on: This Site PubMed Google Scholar Ashley Worthey Ashley Worthey 2MultiCare Yakima Memorial Hospital, Yakima, WA Search for other works by this author on: This Site PubMed Google Scholar Corresponding author: Megan Giruzzi, megan.giruzzi@wsu.edu Clin Diabetes cd230085 https://doi.org/10.2337/cd23-0085 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Citation Megan Giruzzi, Ashley Worthey; Bexagliflozin. Clin Diabetes 2023; cd230085. https://doi.org/10.2337/cd23-0085 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsClinical Diabetes Search Advanced Search This content is only available via PDF. ©2023 by the American Diabetes Association2023Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license. Article PDF first page preview Close Modal You do not currently have access to this content.

Artificial Intelligence (AI) has been increasingly applied in medical imaging for improved accuracy and efficiency. One area that has seen significant advances is the use of AI in the detection of Barrett’s esophagus (BE) and gastric intestinal metaplasia (GIM). BE is a pre-cancerous condition that occurs in the esophagus and can lead to esophageal adenocarcinoma, while GIM is a precancerous lesion that occurs in the stomach. In this review article, we will provide an overview of current AI techniques used in the detection and diagnosis of BE and GIM, including computer-aided diagnosis (CAD) systems, deep learning algorithms, and image analysis techniques. The performance of AI in detecting these conditions will be evaluated and compared to traditional methods. The challenges and limitations of using AI in the diagnosis of BE and GIM will also be discussed. The article aims to provide a comprehensive understanding of the use of AI in BE and GIM detection and to guide future research in this field.

ObjectiveBreast cancer survivors (BCS) are twice as likely to report symptoms of poor sleep as those without cancer. However, sleep disorders are under‐assessed and under‐treated among BCS. The purpose of this study was to determine the portion of BCS who completed referral visits to a sleep specialist and identify the acceptability, facilitators, and barriers to the screening and referral process.MethodsBCS, who reported having sleep problems, completed questionnaires to screen for symptoms suggestive of sleep disorders. Those with symptoms suggestive of sleep apnea, movement disorders, narcolepsy, insomnia syndrome, or circadian disorders, they were referred to a sleep medicine physician or behavioral sleep medicine psychologist. Two months after the referral, participants were interviewed about their perceptions of the acceptability, barriers, and facilitators to sleep screenings and referrals.ResultsOf 34 BCS assessed for eligibility, 29 were eligible and had sleep problems. Only eight of 29 participants (27.6%) completed the sleep referral process. Most thought the screening and referral process was acceptable. However, BCS identified barriers to completing the referral visit, including time, not seeing the need for treatment, insurance/sick leave concerns, and distance/transportation.ConclusionAdequate evaluation and treatment of sleep disorders in BCS are rare. Creative solutions to address barriers to timely sleep referrals are needed to reduce long‐term negative consequences of inadequate sleep.

Using a self-service kiosk to measure blood pressure (BP) has the potential to increase patients' awareness of their BP control and free up medical assistant (MA) time. The objective of this study was to evaluate BP kiosk acceptability and usability, as well as its effects on the workflow of patient BP self-measurement in a primary care clinic. We used qualitative and quantitative assessments of kiosk implementation via meetings with clinic leaders, focus groups with clinic providers and staff, observations of kiosk users, and surveys of kiosk users at 2 and 8 months. Most patients were comfortable using the kiosk (82% at 2 months, 87% at 8 months). Initial provider concerns included accuracy, but most gained confidence after comparing it with other monitors and reviewing the literature supporting its accuracy. Patients and providers saw many benefits: easier BP checks, increased patient engagement, and saved MA time for other tasks. The clinic addressed early concerns (eg, infection control, confusing instructions, perceived loss of personal touch). Most patients (86%) supported the clinic continuing to use the kiosks. Providers, staff, and patients adapted to the use of BP kiosks, providing value by engaging patients in their own care and saving MA time. The clinic decided to keep the self-service kiosk after the pilot period.