scholarly journals Healthcare Data Analytics

2020 ◽  
Author(s):  
Ivana Ognjanovic
Keyword(s):  
Patient Care ◽  
Data Analytics ◽  
Historical Data ◽  
Healthcare Providers ◽  
Diagnostic Procedures ◽  
Health Data ◽  
High Dimensionality ◽  
Healthcare Data

Health analytics is a branch of analysis that focuses on the analysis of complex and large amounts of health data that are characterized by high dimensionality, irregularities and rarities. Their aim is to improve and increase the efficiency of the process of healthcare providers, working with patients, managing costs and resources, improve diagnostic procedures and treatments, etc. The prime focus is investigating historical data and finding templates for different scenarios. As a final product, usually different visualisation tools are produced to support practitioners in patient care to provide better services, and to improve existing procedures.

scholarly journals Improving the quality of healthcare data through information design

2017 ◽  
Vol 23 (1) ◽  
pp. 104-122 ◽  
Author(s):  
Guillermina Noël ◽  
Janet Joy ◽  
Carmen Dyck
Keyword(s):  
Patient Care ◽  
Healthcare Providers ◽  
Improve Patient Care ◽  
Quality Of Healthcare ◽  
Healthcare Data ◽  
Advantages And Disadvantages ◽  
Quality Of Patient Care ◽  
Improve Patient ◽  
Insight Into

Improving the quality of patient care, generally referred to as Quality Improvement (QI), is a constant mission of healthcare. Although QI initiatives take many forms, these typically involve collecting data to measure whether changes to procedures have been made as planned, and whether those changes have achieved the expected outcomes. In principle, such data are used to measure the success of a QI initiative and make further changes if needed. In practice, however, many QI data reports provide only limited insight into changes that could improve patient care. Redesigning standard approaches to QI data can help close the gap between current norms and the potential of QI data to improve patient care. This paper describes our study of QI data needs among healthcare providers and managers at Vancouver Coastal Health, a regional health system in Canada. We present an overview of challenges faced by healthcare providers around QI data collection and visualization, and illustrate the advantages and disadvantages of different visualizations. At present, user– centred and evidence–based design is practically unknown in healthcare QI, and thus offers an important new contribution.

scholarly journals Do You Need My Health Data - Just Ask: Using Blockchain Technology for Collaborative Patient-Centric Health Care

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Tharuka Rupasinghe
Keyword(s):  
Clinical Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Data Access ◽  
Health Data ◽  
Smart Contracts ◽  
Secondary Use ◽  
Blockchain Technology ◽  
Patient Consent ◽  
Patient Centric

IntroductionAcquiring healthcare data for secondary use should benefit from a transparent and highly auditable process when handling patient consent. In the current healthcare infrastructure, the healthcare providers hold the stewardship for the patient data, which includes an authority to determine the data access often without involving the respective patients. The current approach to obtaining patient consent as a one-off task is inadequate to facilitate continuous communication among the patients, healthcare providers and data requestors to manage more personalised data access. Objectives and ApproachThis study explores a novel dynamic patient consent mechanism based on blockchain technology and smart contracts. The aim is to enable patients to actively participate in this process by dynamically managing data consent preferences. Furthermore, it also explores the feasibility of implementing a transparent and auditable collaborative access control infrastructure for clinical data analytics, where all the stakeholders can be involved in determining access to health data. ResultsThe solution has been designed leveraging the blockchain and smart contracts to ensure auditability and transparency of the consent management process where a set of smart contracts controls access to data with minimum human-interferences. In addition, multiple design goals such as data security, privacy, interoperability, legal compliance and accessibility have also been considered when designing a prototype solution. An empirical evaluation is planned to obtain feedback from stakeholders involved in health data sharing for secondary use. Conclusion / ImplicationsThe contribution of this research study is to augment the existing data acquisition procedures for clinical data analytics using blockchain and smart contracts. The proposed novel approach aims to empower and enable patients to play a more active role in controlling access to their data for secondary use. The study will also illuminate the opportunities and challenges which blockchain-based technologies can address related to creating collaborative patient-centric healthcare.

System Architecture of A European Platform for Health Policy Decision Making: MIDAS (Preprint)

2021 ◽  
Author(s):  
Xi Shi ◽  
Gorana Nikolic ◽  
Scott Fischaber ◽  
Michaela Black ◽  
Debbie Rankin ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Policy ◽  
Data Analytics ◽  
Policy Decision ◽  
Health Data ◽  
Social Data ◽  
Policy Makers ◽  
Healthcare Data ◽  
Health Policy Decision ◽  
Policy Decision Making

BACKGROUND Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. OBJECTIVE The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. METHODS The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. RESULTS A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. CONCLUSIONS The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

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.

scholarly journals Best Practices in Specialized Amyloidosis Centers in the United States: A Survey of Cardiologists, Nurses, Patients, and Patient Advocates

2021 ◽  
Vol 15 ◽  
pp. 117954682110152
Author(s):  
Jose Nativi-Nicolau ◽  
Nitasha Sarswat ◽  
Johana Fajardo ◽  
Muriel Finkel ◽  
Younos Abdulsattar ◽  
...  
Keyword(s):  
Best Practices ◽  
Patient Care ◽  
Nurse Practitioners ◽  
Healthcare Providers ◽  
Multidisciplinary Care ◽  
The United States ◽  
Comprehensive Care ◽  
Patient Support ◽  
Patient Support Group ◽  
Patient Advocates

Background: Because transthyretin amyloid cardiomyopathy (ATTR-CM) poses unique diagnostic and therapeutic challenges, referral of patients with known or suspected disease to specialized amyloidosis centers is recommended. These centers have developed strategic practices to provide multidisciplinary comprehensive care, but their best practices have not yet been well studied as a group. Methods: A qualitative survey was conducted by telephone/email from October 2019 to February 2020 among eligible healthcare providers with experience in the management of ATTR-CM at US amyloidosis centers, patients with ATTR-CM treated at amyloidosis centers, and patient advocates from amyloidosis patient support groups. Results: Fifteen cardiologists and 9 nurse practitioners/nurses from 15 selected amyloidosis centers participated in the survey, with 16 patients and 4 patient advocates. Among participating healthcare providers, the most frequently cited center best practices were diagnostic capability, multidisciplinary care, and time spent on patient care; the greatest challenges involved coordination of patient care. Patients described the “ideal” amyloidosis program as one that provides physicians with expertise in ATTR-CM, sufficient time with patients, comprehensive patient care, and opportunities to participate in research/clinical trials. The majority of centers host patient support group meetings, and patient advocacy groups provide support for centers with physician/patient education and research. Conclusions: Amyloidosis centers offer comprehensive care based on staff expertise in ATTR-CM, a multidisciplinary approach, advanced diagnostics, and time dedicated to patient care and education. Raising awareness of amyloidosis centers’ best practices among healthcare providers can reinforce the benefits of early referral and comprehensive care for patients with ATTR-CM.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

scholarly journals Motivational Application of Standardized Antimicrobial Administration Ratios Within a Healthcare System

2020 ◽  
Vol 41 (S1) ◽  
pp. s321-s321
Author(s):  
Stephanie Shealy ◽  
Joseph Kohn ◽  
Emily Yongue ◽  
Casey Troficanto ◽  
Brandon Bookstaver ◽  
...  
Keyword(s):  
Patient Care ◽  
Healthcare System ◽  
Antimicrobial Stewardship ◽  
Broad Spectrum ◽  
Healthcare Providers ◽  
The United States ◽  
Antimicrobial Use ◽  
Gram Positive ◽  
Specific Patient ◽  
Antimicrobial Administration

Background: Hospitals in the United States have been encouraged to report antimicrobial use (AU) to the CDC NHSN since 2011. Through the NHSN Antimicrobial Use Option module, health systems may compare standardized antimicrobial administration ratios (SAARs) across specific facilities, patient care locations, time periods, and antimicrobial categories. To date, participation in the NHSN Antimicrobial Use Option remains voluntary and the value of reporting antimicrobial use and receiving monthly SAARs to multihospital healthcare systems has not been clearly demonstrated. In this cohort study. we examined potential applications of SAAR within a healthcare system comprising multiple local hospitals. Methods: Three hospitals within Prisma Health-Midlands (hospitals A, B, and C) became participants in the NHSN Antimicrobial Use Option in July 2017. SAAR reports were presented initially in October 2017 and regularly (every 3–4 months) thereafter during interprofessional antimicrobial stewardship system-wide meetings until end of study in June 2019. Through interfacility comparisons and by analyzing SAAR categories in specific patient-care locations, primary healthcare providers and pharmacists were advised to incorporate results into focused antimicrobial stewardship initiatives within their facility. Specific alerts were designed to promote early de-escalation of antipseudomonal β-lactams and vancomycin. The Student t test was used to compare mean SAAR in the preintervention period (July through October 2017) to the postintervention period (November 2017 through June 2019) for all antimicrobials and specific categories and locations within each hospital. Results: During the preintervention period, mean SAAR for all antimicrobials in hospitals A, B, and C were 0.69, 1.09, and 0.60, respectively. Notably, mean SAARs at hospitals A, B, and C in intensive care units (ICU) during the preintervention period were 0.67, 1.36, and 0.83 for broad-spectrum agents used for hospital-onset infections and 0.59, 1.27, and 0.68, respectively, for agents used for resistant gram-positive infections. After antimicrobial stewardship interventions, mean SAARs for all antimicrobials in hospital B decreased from 1.09 to 0.83 in the postintervention period (P < .001). Mean SAARs decreased from 1.36 to 0.81 for broad-spectrum agents used for hospital-onset infections and from 1.27 to 0.72 for agents used for resistant gram-positive infections in ICU at hospital B (P = .03 and P = .01, respectively). No significant changes were noted in hospitals A and C. Conclusions: Reporting AU to the CDC NHSN and the assessment of SAARs across hospitals in a healthcare system had motivational effects on antimicrobial stewardship practices. Enhancement and customization of antimicrobial stewardship interventions was associated with significant and sustained reductions in SAARs for all antimicrobials and specific antimicrobial categories at those locations.Funding: NoneDisclosures: None

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