Forecasting, uncertainty and risk; perspectives on clinical decision-making in preventive and curative medicine

Makridakis, Spyros; Kirkham, Richard; Wakefield, Ann; Papadaki, Maria; Kirkham, Joanne; Long, Lisa English

doi:10.1016/j.ijforecast.2017.11.003

Cited by 20 publications

(15 citation statements)

References 43 publications

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“…Intuition is especially helpful for decision-making under time constraints and with insufficient information [ 10 ]. However, humans may have a different experience and even experts are vulnerable to cognitive biases [ 11 , 12 ] which causes substantial variability in decision-making even in cases classified as easy [ 13 ]. With the amount of information growing in more complex cases, prognostication based on heuristics becomes more prone to errors, i.e.…”

Section: State Of the Artmentioning

confidence: 99%

On predictions in critical care: The individual prognostication fallacy in elderly patients

Beil

Sviri

Lange

et al. 2021

Journal of Critical Care

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Predicting the future course of critical conditions involves personal experience, heuristics and statistical models. Although these methods may perform well for some cases and population averages, they suffer from substantial shortcomings when applied to individual patients. The reasons include methodological problems of statistical modeling as well as limitations of cross-sectional data sampling. Accurate predictions for individual patients become crucial when they have to guide irreversible decision-making. This notably applies to triage situations in response to a lack of healthcare resources. We will discuss these issues and argue that analysing longitudinal data obtained from time-limited trials in intensive care can provide a more robust approach to individual prognostication.

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Section: State Of the Artmentioning

confidence: 99%

On predictions in critical care: The individual prognostication fallacy in elderly patients

Beil

Sviri

Lange

et al. 2021

Journal of Critical Care

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“…Healthcare researchers and policymakers increasingly emphasize SDM for quality treatment [31]. Collaborative medical decision-making has been a common practice in healthcare industries [14][15][16][17][18][19]21,22,55]. Schwartz [56] defines collaborative medical decision-making as:…”

Section: Collaborative Medical Decision-making and Blockchainmentioning

confidence: 99%

“…Around 63% of patients visit an online platform for medical help and 62.4% of them trust their doctor for data sharing [13]. Lack of participants, sloppy stakeholder's identity securing mechanisms, corruption in decision picking mechanisms, lack of accountability, data manipulation, delayed communication, and corrupted mediums have been recognized as the major worries of collaborative medical decision-making [14][15][16][17][18][19]. Despite aforesaid challenges, collaboration can always upgrade the existing healthcare amenity.…”

Section: Introductionmentioning

confidence: 99%

“…Clinical data, medical decisions, and patient identities are being manipulated and distributed without proper acknowledgment and consent. Existing collaborative healthcare decision-making has the following limitations [14][15][16][17][18][19][21][22][23]:…”

mentioning

confidence: 99%

“…The time delay in existing collaborative medical decision making.Existing collaborative healthcare decision-making has the following limitations[14][15][16][17][18][19][21][22][23]:• Multi-dimensionalpersonal data privacy policies imposed by individual institutes. • Clinical data, medical decisions, and patient identities are being manipulated and distributed without proper acknowledgment and consent.…”

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confidence: 99%

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Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making

et al. 2019

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The current healthcare sector is facing difficulty in satisfying the growing issues, expenses, and heavy regulation of quality treatment. Surely, electronic medical records (EMRs) and protected health information (PHI) are highly sensitive, personally identifiable information (PII). However, the sharing of EMRs, enhances overall treatment quality. A distributed ledger (blockchain) technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Privacy, security, and lack of confidence among stakeholders are the main downsides of extensive medical collaboration. This study, therefore, utilizes the transparency, security, and efficiency of blockchain technology to establish a collaborative medical decision-making scheme. This study considers the experience, skill, and collaborative success rate of four key stakeholders (patient, cured patient, doctor, and insurance company) in the healthcare domain to propose a local reference-based consortium blockchain scheme, and an associated consensus gathering algorithm, proof-of-familiarity (PoF). Stakeholders create a transparent and tenable medical decision to increase the interoperability among collaborators through PoF. A prototype of PoF is tested with multichain 2.0, a blockchain implementing framework. Moreover, the privacy of identities, EMRs, and decisions are preserved by two-layer storage, encryption, and a timestamp storing mechanism. Finally, superiority over existing schemes is identified to improve personal data (PII) privacy and patient-centric outcomes research (PCOR).

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Decision-Making Under Resource Constraints

Beil

Heerden

Sviri

2022

The Very Old Critically Ill Patients

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Forecasting, uncertainty and risk; perspectives on clinical decision-making in preventive and curative medicine

Cited by 20 publications

References 43 publications

On predictions in critical care: The individual prognostication fallacy in elderly patients

On predictions in critical care: The individual prognostication fallacy in elderly patients

Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making

Decision-Making Under Resource Constraints

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