A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data

2013 ◽  
Vol 35 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Fei Wang ◽  
Noah Lee ◽  
Jianying Hu ◽  
Jimeng Sun ◽  
Shahram Ebadollahi ◽  
...  
Keyword(s):  
Event Sequences ◽  
Healthcare Data

Public Reporting of Cardiovascular Data: Benefits, Pitfalls, and Vision for the Future

2016 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Gregory J Dehmer ◽  
Keyword(s):  
Clinical Data ◽  
Clinical Status ◽  
Public Reporting ◽  
Professional Organizations ◽  
Healthcare Data ◽  
Outcomes Of Care ◽  
The Future ◽  
Policy Statements ◽  
Quality And Outcomes ◽  
Reporting Programs

Public reporting of healthcare data is not a new concept. This initiative continues to proliferate as consumers and other stakeholders seek information on the quality and outcomes of care. Furthermore, mandates for the development of additional public reporting efforts are included in several new healthcare legislations such as the Affordable Care Act. Many current reporting programs rely heavily on administrative data as a surrogate for true clinical data, but this approach has well-defined limitations. Clinical data are traditionally more difficult and costly to collect, but more accurately reflect the clinical status of the patient, thus enhancing validity of the quality metrics and the reporting program. Several professional organizations have published policy statements articulating the main principles that should establish the foundation for public reporting programs in the future.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Healthcare Data Warehousing

2017 ◽  
Vol 4 (4) ◽  
pp. 1
Author(s):  
ARUNACHALAM S. ◽  
PAGE TOM ◽  
THORSTEINSSON G. ◽  
◽  
◽  
...  
Keyword(s):  
Data Warehousing ◽  
Healthcare Data

Need and Importance of Healthcare Data Integrity

2019 ◽  
Vol 11 (4) ◽  
pp. 854-859 ◽  
Author(s):  
Alka Agrawal ◽  
Nawaf Rasheed Alharbe
Keyword(s):  
Data Integrity ◽  
Healthcare Data

scholarly journals Changes in daily mental health service use and mortality at the commencement and lifting of COVID-19 ‘lockdown’ policy in 10 UK sites: a regression discontinuity in time design

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e049721
Author(s):  
Ioannis Bakolis ◽  
Robert Stewart ◽  
David Baldwin ◽  
Jane Beenstock ◽  
Paul Bibby ◽  
...  
Keyword(s):  
Mental Health ◽  
Inpatient Care ◽  
Service Use ◽  
Regression Discontinuity ◽  
Mental Healthcare ◽  
Service Level ◽  
Community Services ◽  
Mental Health Service Use ◽  
Healthcare Data ◽  
Early Intervention In Psychosis

ObjectivesTo investigate changes in daily mental health (MH) service use and mortality in response to the introduction and the lifting of the COVID-19 ‘lockdown’ policy in Spring 2020.DesignA regression discontinuity in time (RDiT) analysis of daily service-level activity.Setting and participantsMental healthcare data were extracted from 10 UK providers.Outcome measuresDaily (weekly for one site) deaths from all causes, referrals and discharges, inpatient care (admissions, discharges, caseloads) and community services (face-to-face (f2f)/non-f2f contacts, caseloads): Adult, older adult and child/adolescent mental health; early intervention in psychosis; home treatment teams and liaison/Accident and Emergency (A&E). Data were extracted from 1 Jan 2019 to 31 May 2020 for all sites, supplemented to 31 July 2020 for four sites. Changes around the commencement and lifting of COVID-19 ‘lockdown’ policy (23 March and 10 May, respectively) were estimated using a RDiT design with a difference-in-difference approach generating incidence rate ratios (IRRs), meta-analysed across sites.ResultsPooled estimates for the lockdown transition showed increased daily deaths (IRR 2.31, 95% CI 1.86 to 2.87), reduced referrals (IRR 0.62, 95% CI 0.55 to 0.70) and reduced inpatient admissions (IRR 0.75, 95% CI 0.67 to 0.83) and caseloads (IRR 0.85, 95% CI 0.79 to 0.91) compared with the pre lockdown period. All community services saw shifts from f2f to non-f2f contacts, but varied in caseload changes. Lift of lockdown was associated with reduced deaths (IRR 0.42, 95% CI 0.27 to 0.66), increased referrals (IRR 1.36, 95% CI 1.15 to 1.60) and increased inpatient admissions (IRR 1.21, 95% CI 1.04 to 1.42) and caseloads (IRR 1.06, 95% CI 1.00 to 1.12) compared with the lockdown period. Site-wide activity, inpatient care and community services did not return to pre lockdown levels after lift of lockdown, while number of deaths did. Between-site heterogeneity most often indicated variation in size rather than direction of effect.ConclusionsMH service delivery underwent sizeable changes during the first national lockdown, with as-yet unknown and unevaluated consequences.

scholarly journals Healthcare Data for Achieving a More Personalized Treatment of Chronic Kidney Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 488
Author(s):  
Francisco Herrera-Gómez ◽  
F. Javier Álvarez
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Prognostic Markers ◽  
Observational Evidence ◽  
Personalized Treatment ◽  
Special Issue ◽  
Healthcare Data ◽  
Clinical Endpoints ◽  
Risk Patients

The current concept of healthcare incites a more personalized treatment of diseases. To this aim, biomarkers are needed to improve decision-making facing chronic kidney disease (CKD) patients. Prognostic markers provided by real-world (observational) evidence are proposed in this Special Issue entitled “Biomarkers in Chronic Kidney Disease”, with the intention to identify high-risk patients. These markers do not target measurable parameters in patients but clinical endpoints that may be in turn transformed to benefits under the effect of future interventions.

scholarly journals Regional medical practice variation in high-cost healthcare services

2021 ◽  
Author(s):  
Michael Berger ◽  
Thomas Czypionka
Keyword(s):  
Medical Practice ◽  
Regional Variation ◽  
Multivariate Regression ◽  
Healthcare Services ◽  
Practice Variation ◽  
District Level ◽  
Healthcare Data ◽  
Regional Medical ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

AbstractMagnetic resonance imaging (MRI) is a popular yet cost-intensive diagnostic measure whose strengths compared to other medical imaging technologies have led to increased application. But the benefits of aggressive testing are doubtful. The comparatively high MRI usage in Austria in combination with substantial regional variation has hence become a concern for its policy makers. We use a set of routine healthcare data on outpatient MRI service consumption of Austrian patients between Q3-2015 and Q2-2016 on the district level to investigate the extent of medical practice variation in a two-step statistical analysis combining multivariate regression models and Blinder–Oaxaca decomposition. District-level MRI exam rates per 1.000 inhabitants range from 52.38 to 128.69. Controlling for a set of regional characteristics in a multivariate regression model, we identify payer autonomy in regulating access to MRI scans as the biggest contributor to regional variation. Nevertheless, the statistical decomposition highlights that more than 70% of the regional variation remains unexplained by differences between the observable district characteristics. In the absence of epidemiological explanations, the substantial regional medical practice variation calls the efficiency of resource deployment into question.

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