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Cancer registry and big data exchange
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Zhenwei Shi, Leonard Wee, Andre Dekker
To be effective in clinical decision support, predictive models must be able to estimate the probability of a given outcome over a range of clinical situations. Therefore, an approach that integrates the data of many patients over different treatment settings is essential. Predictive outcome models have the potential to improve quality of life, identify patients at high (or low) risk, and to prolong the survival of patients with cancer (Dehing-Oberije et al. 2009, 2010, 2011; Oberije et al. 2014). Some predictive outcome models for various cancer sites can be found at http://www.predictcancer.org. These prediction models support the practice of personalized radiotherapy that is tailored to the individual risk profile of the patient. If multiple treatment possibilities exist where clinical evidence is equivocal, patient and physician preferences for certain outcomes may play a role. The collaborative consultation process between a patient and their treatment physician is known as shared decision making (Elwyn et al. 2012; Stacey et al. 2014). Reliable model-based predictions of potential treatment responses are a prerequisite for information trade-offs between the risks of harm and strength of treatment in the shared decision-making paradigm. In keeping with RLHC, observational data on quality-of-life, patient-reported outcomes, and decision regret should put back into the web of clinical knowledge, so that continually updated models are better able to inform physician’s and patients’ decisions.
Wales
Published in Braithwaite Jeffrey, Mannion Russell, Matsuyama Yukihiro, Shekelle Paul, Whittaker Stuart, Al-Adawi Samir, Health Systems Improvement Across the Globe: Success Stories from 60 Countries, 2017
Shared decision-making is “a process in which clinicians and patients work together to select tests, treatments, management, or support packages, based on clinical evidence and the patient’s informed preferences. It involves the provision of evidence-based information about options and possible outcomes, together with decision support counseling and a system for recording and implementing patients’ informed preferences” (Coulter and Collins, 2011, vii). When decision-making is shared between healthcare professionals and patients, decision quality and patient satisfaction are improved and, in some cases, result in more cost-effective care.
Consumer Health Information Technology
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Teresa Zayas-Cabán, P. Jon White
Clinical decision-making is one important aspect of care delivery because it involves the individual receiving care or their caregivers. Shared decision-making tools encourage and facilitate communication between clinician and patient and can document how the decision was reached (Finkelstein et al., 2012). These tools can increase consumer understanding of a condition or treatment and improve adherence to recommended management.
Digital Transformation in Healthcare: Insights on Value Creation
Published in Journal of Computer Information Systems, 2023
Kaushik Ghosh, Michael S. Dohan, Hareesh Veldandi, Monica Garfield
Shared decision making entails the reaching of a consensus between physician, patient, and other decision makers such as family, for a plan of treatment.38 This is seen as an approach that respects patient autonomy and the right to informed consent. As shared decision making entails the exchange of data between parties, data collected by the patient helps doctors to spend less time explaining and more time giving guidance. Participant #4 Explains: We are trying to do a lot more digitization whereby previously our clinicians are spending more time collecting the data, and now they’re spending more time analyzing the data, and based on the alerts, actually spending time with the family to say hey I’ve already looked at your data, here’s what I’m seeing and this is what you need to do, in order to take better care of your child.
Frontiers of medical decision-making in the modern age of data analytics
Published in IISE Transactions, 2023
Consumer choice models have been extensively applied in the marketing domain to understand how product features influence consumer purchasing behavior. However, their application in the healthcare domain has been more limited, with some notable exceptions (e.g., a study by Steimle et al. (2022) on student preferences for the return to classes in the context of COVID-19). Recent years have seen increasing acceptance of the shared decision-making framework in which physicians work with patients to make decisions consistent with a patient's perspective on risk vs. reward tradeoffs. For example, many options may be available for treatment in the context of cancer, including active surveillance, surgery, or radiation therapy. Embedding the potential for individual patient choice within IE/OR models for the design of screening strategies, for example, could inform strategy design decisions by accounting for patient preferences using discrete choice models (see Ryan et al. (2007) for a review).
Secure decentralized decisions to enhance coordination in consolidated hospital systems
Published in IISE Transactions on Healthcare Systems Engineering, 2020
Adrien Badré, Shima Mohebbi, Leili Soltanisehat
A new solution to enhance coordination in patients assignment systems is the shared decision making, in which multiple healthcare parties such as physicians and patients make decisions jointly, using the best available evidences (see Alegria et al., 2018; Bai, Gopal, Nunez, & Zhdanov, 2014; Elf, Fröst, Lindahl, & Wijk, 2015; Hughes et al., 2018; Torbica, Fattore, & Ayala, 2014). Shared decision making can improve both the efficiency of the decision making and the ethical imperative due to the patient’s rights, while it reduces unwarranted healthcare practice variations (Legare et al., 2014). Nonetheless, shared decision making can be effective only when the healthcare providers can access the data in the right time and can collaborate through a fast and trustable channel (Agoritsas et al., 2015).This process will become more difficult if the healthcare providers use different databases, and communication networks. Therefore, data availability and interoperability among healthcare providers can be an important factor influencing the effectiveness of shared decision making in patients assignment systems. To this end, this paper proposes a Decentralized Patients Assignment System (DPAS) framework utilizing blockchain technology coupled with machine learning algorithms and integer programing.