One barrier of using data analytics related to improving patient outcomes is the lack of interoperability between different types of health systems – both within individual countries and across international borders – resulting in fragmented access and use of medical records which hinders meaningful comparison between practices or tracking important clinical improvements over time (Zheng et al., 2020). A second barrier involves incomplete or inaccurate records due to manual entry errors or due to different standards being applied across organizations (Gillam & Marchant-Forde 2020). Finally inadequate resources available for training staff on how best utilize the technology may limit its effectiveness (Kirkpatrick et al., 2019).

Several national initiatives have been developed over recent years aimed at utilizing big data analytics within healthcare settings with a focus on improving patient outcomes such as The Center For Medicare And Medicaid Innovation’s Accountable Care Organizations initiative (ACOs) which focuses on providing incentives for primary care providers who provide comprehensive coordinated services leading to better cost efficiency while maintaining high quality standards (Scharfstein et al., 2017); The National Quality Strategy also seeks promote accountability through transparent reporting based on quality measure performance metrics; The Health Information Technology For Economic And Clinical Health Act requires all hospitals adopt electronic medical record systems by 2021; And The Office Of The National Coordinator For Health IT has created several programs focused on advancing research into innovative technologies including those aimed at enhancing population health management using predictive models built upon large datasets collected from various sources including hospitals clinics laboratories etc. Financial incentives are also available through these various initiatives as well as other government funded projects such as grants available via agencies like HHS’s Centers For Medicare And Medicaid Services .

Accreditation expectations regarding utilization of big data include certification requirements under Meaningful Use Stage 3 which states that eligible physicians must implement advanced clinical decision support tools assistive computer applications capable machine learning models etc in order increase operational efficiencies reduce costs improve quality control measure performance against established goals etc (CMS 2018). Additionally many Joint Commission accredited organizations have specific documentation requirements involving both traditional charts/records but also digital ones like EMRs/EHRs designed specifically towards measuring progress towards achieving desired clinical results thereby further demonstrating compliance with evidence-based measurement criteria .

Two software options for analyzing large datasets include Tableau Desktop & Server editions which allow users create sophisticated interactive dashboards quickly visualize relationships among multiple variables apply predictive modeling algorithms uncover outliers among groups easily sort out trends while SAS Enterprise Miner provides more advanced statistical capabilities by allowing users construct powerful mining algorithms using metadata identification processes execute detailed analyses perform powerful feature engineering experiments streamline customer segmentation segment customer lifecycle stages discover complex interactions analyze customer profitability etc . An example study investigating improvement in patient outcome was conducted by Salim et al.,(2020) looking at the effects introducing telemedicine had when it came treating patients with Multiple Sclerosis specifically focusing upon whether expanding availability this field resulted increased satisfaction amongst individuals receiving treatment participants scoring higher ratings access convenience satisfaction overall service experience willingness recommend service their friends family members . Ethical issues surrounding utilization big datainclude transparency confidentiality security concerns privacy rights informed consent responsibility sharing knowledge others benefits derived misuse/unauthorized disclosure derived information etc . Questions should raised regard how personal identifiable information being handled particularly if outside third parties involved collecting storing aggregating interpreting analyzing disseminating same Security & privacy considerations should taken account example proper authentication procedures encryption techniques secure transmission protocols authentication mechanisms prevent unauthorized access protect sensitive private info stored databases cloud computing platforms employee educational requirements proper disposal retired hardware devices properly securing mobile phones tablets laptops computers smartphones IoT devices risk assessment strategies auditing frameworks proper lockdown procedures incident response preparedness plans adequate breach notification methodologies periodic vulnerability assessments disaster recovery planning measures restrict physical location devices access ensure personnel understand HIPPA/PCI DSS compliant regulations put place Recommendations could made reducing recidivism rates decreasing hospital readmission times minimizing potential duplicate orders avoid costly medications errors implementing real-time surveillance protocols developing early warning alert systems identifying adverse drug reactions sooner leveraging existing EHR infrastructure automatically capture vitals signs monitor disease progression establishing stronger communication pathways linking specialist centers enabling proactive personalized treatments helping identify gaps treatment plans earlier implementing track trace systems ensure correct medication delivered right person right time increasing frequency follow-ups monitoring long-term therapeutic responses enabling remote symptom monitoring arrange house calls elderly frail patients increasing effectiveness preventive screenings predicting disease onset determining optimal medication dosage intervals conducting larger scale epidemiological studies stratifying populations according shared characteristics estimating future resource needs financial forecasting purposes conducting market research targeting effective interventions mitigating pollution levels studying gene level diseases understanding social determinants health predicting demand medical services structuring pricing policies accordingly optimizing health plan design