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Health Information Technology
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Joseph P. Cook, Gabriel Jipa, Claudia Zavala, Lobna A. Salem
Within the broad scope of AI, there are several key technologies that are taking center stage, especially in relation to healthcare (Mindfields, 2018). For instance, Natural Language Processing (NLP) entails taking unstructured data such as electronic health records (EHRs), clinical notes, journal articles, and other valuable information that may not be linked, and creating meaning out of it by highlighting relevant information. NLP allows for this to be done quickly and seamlessly, where it would take several HCPs many hours to tie all of this information together and pinpoint the links between the data. Another important technology within AI is deep learning (DL). This entails training a machine with previously available information until the model is “locked”, and the machine can then utilize these complex algorithms to perform tasks like diagnosis, prediction, and treatment selection. Thirdly, Context Aware Processing (CAP) can help create efficient chatbots to greatly reduce the burden of HCPs in responding to all patients in real time. They can be used to direct patients to the right HCP and even provide accurate solutions to simple problems. Finally, intelligent robotics can be used in developing tools for surgical procedures, or in developing companions for those suffering from mental illness of cognitive decay due to lack of interaction with others.
Digital Therapeutics for Sleep and Mental Health
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Peter Hames, Christopher B. Miller
The partnership with CVS Health provides a payment processing that integrates with self-insured employers' existing processes and systems. Eligibility and payment processing for Sleepio enrollees is handled using the same infrastructure as any other reimbursed product offered via CVS PBM. Claims are processed just like a drug, and self-insured companies are only billed when an employee signs up to use the product.
Blockchain Primer
Published in Salvatore Volpe, Health Informatics, 2022
There are additional projects underway inside most large insurance companies, pharmaceutical firms, and supply chain firms. Claims processing has been identified as a target for blockchain disruption or enhancement, inclusive of streamlining preauthorization submissions, health insurance claims adjudication, and eligibility management.93,94
The effects of diurnal variability and modality on false memories formation
Published in Chronobiology International, 2023
Justyna M. Olszewska, Amy E. Hodel, Anna Ceglarek, Magdalena Fafrowicz
Results for recognition memory in STM in both the morning and evening hours are supported by two main theoretical domains: (a) types of processing and (b) verbatim and gist traces on memory performance (Reyna and Brainerd 1995). Folkard’s (1979) proposed a shift in processing between the morning and evening hours. More specifically, participants engage in maintenance processing earlier in the day, which allows for the allocation of attention to the characteristics and details of the stimulus. Later, in the day, Folkard (1979) attributes memory performance to elaborative processing, where attention is directed at the meaning of the stimulus. In the current study, when participants are presented semantic material in the morning hours, we argue that it is the phonetic properties of the word that are most helpful, whereas, in the evening, it is the meaning of the words that are most memorable. Second, it is well known that both gist (general interpretations) and verbatim traces (precise representations) collectively support accurate memory for items previously studied (Reyna and Brainerd 1995). These two factors combined likely led to greater accuracy of the studied items in STM.
Discovering hidden patterns among medicines prescribed to patients using Association Rule Mining Technique
Published in International Journal of Healthcare Management, 2023
Hospital pharmacy is an essential department in hospitals. It manages the medicine replenishment services. It includes procurement, storage, processing medication orders, dispensing medicines to all patients ensuring the availability of medications at affordable prices [1]. However, pharmacists find it challenging to estimate the actual medication demand. It is hard for them to decide the effective replenishment strategies and the appropriate inventory control policy [2]. Hence, hospital pharmacy management suffers from inefficient processes [3]. This inefficiency results in a mismatch between demand and supply. It also leads to time-consuming manual tasks, out-of-stock situations, medicine expiration, and high operating costs [4]. Besides, the complexities in the healthcare sector are unique and challenging. For instance, each patient arriving in a hospital for treatment and care may suffer from either single or multiple health issues and may need one or more medicines in different forms (oral/injection), dosages and combinations [5]. The medication demand prediction and replenishment decisions are extremely difficult for hospital pharmacists [4]. Apart from that, in situations of multiple medicine administration, shortages of any supporting medicines result in a delay in treatment (and piling up of main medicine inventory). The reason is that the medication order may contain combinations of medications causing associations among prescribed medicines [6].
A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting
Published in Expert Opinion on Drug Safety, 2022
Raymond Li, Kate Curtis, Syed Tabish Zaidi, Connie Van, Ronald Castelino
Digital initiatives have been introduced in the last decade to transform the management of patients in healthcare setting. Examples of these include the adoption of eMedical Records, eMedication Management, ePrescribing, digital health records, and mobile apps [17–20]. Natural language processing and artificial intelligence in healthcare have also been introduced in areas of clinical decision support, information management, data analysis of electronic health records for diagnosis, as well as the provision of personalized healthcare [21,22]. These measures can support improvements in adherence to guidelines for healthcare professionals, increase cost savings, enhance patient satisfaction, and promote efficiency across hospital processes. Therefore, there exists an opportunity to leverage digital technologies to improve the process and experience of reporting ADRs.