Wearables in Schizophrenia: An Update on Current and Future Clinical Applications (Preprint)

2021 ◽  
Author(s):  
Lakshan N Fonseka ◽  
Benjamin K P Woo
Keyword(s):  
Real Time ◽  
Sleep Disturbances ◽  
Clinical Decision Making ◽  
Recent Literature ◽  
Disease Onset ◽  
Clinical Decision ◽  
Psychotic Symptoms ◽  
First Episode ◽  
World Population ◽  
Patient Acceptability

UNSTRUCTURED Schizophrenia affects 1% of the world population and is associated with a reduction in life expectancy of 20 years. The increasing prevalence of both consumer and clinical-grade wearable technology offers new metrics to guide clinical decision-making remotely and in real-time. Herein, the recent literature on wearables is reviewed for potential clinical utility in schizophrenia, including diagnosis and first episode psychosis, relapse prevention, and patient acceptability. Several studies have further confirmed the validity of various devices in their ability to track sleep, an especially useful metric in schizophrenia as sleep disturbances may be predictive of disease onset or an acute worsening of psychotic symptoms. Through machine learning, wearable-obtained heart rate and motor activity was able to differentiate between controls and schizophrenia patients. Wearables can capture the autonomic dysregulation that has been detected when patients are actively experiencing paranoia, hallucinations, or delusions. Multiple platforms are currently being researched such as Health Outcomes through Positive Engagement and Self-Empowerment (HOPES), Mobile Therapeutic Attention for Treatment Resistant Schizophrenia (m-RESIST), and Sleepsight that may ultimately link patient data to clinicians. The future is bright for wearables in schizophrenia, as the recent literature exemplifies their potential to offer real-time insights to guide diagnosis and management.

scholarly journals Dilemmas in the treatment of early-onset first-episode psychosis

2018 ◽  
Vol 8 (8) ◽  
pp. 231-239 ◽  
Author(s):  
Daniel Hayes ◽  
Marinos Kyriakopoulos
Keyword(s):  
Young People ◽  
Early Onset ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
First Episode Psychosis ◽  
Psychotic Symptoms ◽  
First Episode ◽  
Close Monitoring ◽  
Optimal Outcomes ◽  
Episode Psychosis

Early-onset first-episode psychosis (EOP) is a severe mental disorder that can pose a number of challenges to clinicians, young people and their families. Its assessment and differentiation from other neurodevelopmental and mental health conditions may at times be difficult, its treatment may not always lead to optimal outcomes and can be associated with significant side effects, and its long-term course and prognosis seem to be less favourable compared with the adult-onset disorder. In this paper, we discuss some dilemmas associated with the evaluation and management of EOP and propose approaches that can be used in the clinical decision-making process. A detailed and well-informed assessment of psychotic symptoms and comorbidities, a systematic approach to treatment with minimum possible medication doses and close monitoring of its effectiveness and adverse effects, and multidimensional interventions taking into consideration risks and expectations associated with EOP, are paramount in the achievement of the most favourable outcomes for affected children and young people.

scholarly journals Single-copy detection of somatic variants from solid and liquid biopsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana-Luisa Silva ◽  
Paulina Klaudyna Powalowska ◽  
Magdalena Stolarek ◽  
Eleanor Ruth Gray ◽  
Rebecca Natalie Palmer ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Real Time Pcr ◽  
Clinical Decision Making ◽  
High Sensitivity ◽  
Clinical Decision ◽  
Single Copy ◽  
Turnaround Time ◽  
Copy Detection ◽  
Allele Specific

AbstractAccurate detection of somatic variants, against a background of wild-type molecules, is essential for clinical decision making in oncology. Existing approaches, such as allele-specific real-time PCR, are typically limited to a single target gene and lack sensitivity. Alternatively, next-generation sequencing methods suffer from slow turnaround time, high costs, and are complex to implement, typically limiting them to single-site use. Here, we report a method, which we term Allele-Specific PYrophosphorolysis Reaction (ASPYRE), for high sensitivity detection of panels of somatic variants. ASPYRE has a simple workflow and is compatible with standard molecular biology reagents and real-time PCR instruments. We show that ASPYRE has single molecule sensitivity and is tolerant of DNA extracted from plasma and formalin fixed paraffin embedded (FFPE) samples. We also demonstrate two multiplex panels, including one for detection of 47 EGFR variants. ASPYRE presents an effective and accessible method that simplifies highly sensitive and multiplexed detection of somatic variants.

scholarly journals Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

2018 ◽  
Author(s):  
Robert Moss ◽  
Alexander E Zarebski ◽  
Sandra J Carlson ◽  
James M McCaw
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Healthcare Seeking ◽  
Public Health Decision ◽  
Predictive Skill ◽  
Health Decision ◽  
Substantial Change

AbstractFor diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

scholarly journals Real-time utilisation of administrative data in the ED to identify older patients at risk: development and validation of the Dynamic Silver Code

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033374 ◽  
Author(s):  
Daniela Balzi ◽  
Giulia Carreras ◽  
Francesco Tonarelli ◽  
Luca Degli Esposti ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
At Risk ◽  
Cohort Study ◽  
Real Time ◽  
Administrative Data ◽  
Older Patients ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Patients At Risk ◽  
Ed Visits

ObjectiveIdentification of older patients at risk, among those accessing the emergency department (ED), may support clinical decision-making. To this purpose, we developed and validated the Dynamic Silver Code (DSC), a score based on real-time linkage of administrative data.Design and settingThe ‘Silver Code National Project (SCNP)’, a non-concurrent cohort study, was used for retrospective development and internal validation of the DSC. External validation was obtained in the ‘Anziani in DEA (AIDEA)’ concurrent cohort study, where the DSC was generated by the software routinely used in the ED.ParticipantsThe SCNP contained 281 321 records of 180 079 residents aged 75+ years from Tuscany and Lazio, Italy, admitted via the ED to Internal Medicine or Geriatrics units. The AIDEA study enrolled 4425 subjects aged 75+ years (5217 records) accessing two EDs in the area of Florence, Italy.InterventionsNone.Outcome measuresPrimary outcome: 1-year mortality. Secondary outcomes: 7 and 30-day mortality and 1-year recurrent ED visits.ResultsAdvancing age, male gender, previous hospital admission, discharge diagnosis, time from discharge and polypharmacy predicted 1-year mortality and contributed to the DSC in the development subsample of the SCNP cohort. Based on score quartiles, participants were classified into low, medium, high and very high-risk classes. In the SCNP validation sample, mortality increased progressively from 144 to 367 per 1000 person-years, across DSC classes, with HR (95% CI) of 1.92 (1.85 to 1.99), 2.71 (2.61 to 2.81) and 5.40 (5.21 to 5.59) in class II, III and IV, respectively versus class I (p<0.001). Findings were similar in AIDEA, where the DSC predicted also recurrent ED visits in 1 year. In both databases, the DSC predicted 7 and 30-day mortality.ConclusionsThe DSC, based on administrative data available in real time, predicts prognosis of older patients and might improve their management in the ED.

scholarly journals Utility and Acceptability of Influenza Surveillance amongst Emergency Providers

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea F. Dugas ◽  
Howard Burkom ◽  
Anna L. DuVal ◽  
Richard Rothman
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Additional Information ◽  
Provider Surveys

We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment.

scholarly journals Artificial intelligence in arthroplasty

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Glen Purnomo ◽  
Seng-Jin Yeo ◽  
Ming Han Lincoln Liow
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Recent Literature ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Patient Specific ◽  
Allocation Decision ◽  
Medical Specialties ◽  
Future Direction ◽  
Improve Patient

AbstractArtificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

scholarly journals Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

2019 ◽  
Vol 4 (1) ◽  
pp. 12 ◽  
Author(s):  
Robert Moss ◽  
Alexander Zarebski ◽  
Sandra Carlson ◽  
James McCaw
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Community Level ◽  
Surveillance Systems ◽  
Healthcare Seeking ◽  
Public Health Decision ◽  
Predictive Skill ◽  
Health Decision

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.

Dashboard to Support the Decision-Making within a Chronic Disease

2012 ◽  
pp. 160-171 ◽  
Author(s):  
Leonor Teixeira ◽  
Vasco Saavedra ◽  
João Pedro Simões
Keyword(s):  
Decision Making ◽  
Chronic Disease ◽  
Real Time ◽  
Clinical Decision Making ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Information And Communications Technologies ◽  
The Right ◽  
Real Time Information

This chapter describes a monitoring system based on alerts and Key Performance Indicators (KPIs), applied in clinical context, within a chronic disease (haemophilia). This kind of disease follows the patient through his/her life, and its treatment requires an almost permanent exchange of data/information with healthcare professional (HCPs), with the information and communications technologies (ICTs) a key contribution in this process. However, most applications based on those ICTs do not allow the analysis of heterogeneous data in real-time, requiring the availability of clinicians to check the data and analyze the information to support the clinical decision process. Since time is a scarce resource in the context of healthcare providers, and information a crucial resource in the decision support process, real-time monitoring systems can help finding the right balance between those two resources, presenting the key information in an appropriate format, through alerts and KPIs. The system described in this chapter, named hemo@care_dashboard, aims to support clinical decision-making of healthcare professionals of a specific chronic disease, providing real-time information in a push-logic through alerts and KPIs, displayed on a dashboard.

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