scholarly journals Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021

2021 ◽  
pp. 219256822110084
Author(s):  
Jacob K. Greenberg ◽  
Ayodamola Otun ◽  
Zoher Ghogawala ◽  
Po-Yin Yen ◽  
Camilo A. Molina ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Data Analytics ◽  
Biomedical Informatics ◽  
Surgical Care ◽  
Implementation Strategies ◽  
Narrative Review ◽  
Machine Learning Techniques ◽  
Billing Data ◽  
Regulatory Oversight ◽  
Analytical Approaches

Study Design: Narrative review. Objectives: There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. Methods: We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. Results: A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. Conclusions: Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.

scholarly journals Ambulatory Surgical Centers: Improving Quality of Operative Spine Care?

2020 ◽  
Vol 10 (1_suppl) ◽  
pp. 29S-35S ◽  
Author(s):  
Christopher D. Witiw ◽  
Jefferson R. Wilson ◽  
Michael G. Fehlings ◽  
Vincent C. Traynelis
Keyword(s):  
Health Care Providers ◽  
Spine Surgery ◽  
Cost Optimization ◽  
Safety Data ◽  
Case Series ◽  
Surgical Care ◽  
Narrative Review ◽  
Care Providers ◽  
Ambulatory Surgical Centers

Study Design: Narrative review with commentary. Objective: Present healthcare reform focuses on cost-optimization and quality improvement. Spine surgery has garnered particular attention; owing to its costly nature. Ambulatory Surgical Centers (ASC) present a potential avenue for expenditure reduction. While the economic advantage of ASCs is being defined, cost saving should not come at the expense of quality or safety. Methods: This narrative review focuses on current definitions, regulations, and recent medical literature pertinent to spinal surgery in the ASC setting. Results: The past decade witnessed a substantial rise in the proportion of certain spinal surgeries performed at ASCs. This setting is attractive from the payer perspective as remuneration rates are generally less than for equivalent hospital-based procedures. Opportunity for physician ownership and increased surgeon productivity afforded by more specialized centers make ASCs attractive from the provider perspective as well. These factors serve as extrinsic motivators which may optimize and improve quality of surgical care. Much data supports the safety of spine surgery in the ASC setting. However, health care providers and policy makers must recognize that current regulations regarding safety and quality are less than comprehensive and the data is predominately from selected case-series or comparative cohorts with inherent biases, along with ambiguities in the definition of “outpatient.” Conclusions: ASCs hold promise for providing safe and efficient surgical management of spinal conditions; however, as more procedures shift from the hospital to the ASC rigorous quality and safety data collection is needed to define patient appropriateness and track variability in quality-related outcomes.

scholarly journals Detection of untreated sewage discharges to watercourses using machine learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter Hammond ◽  
Michael Suttie ◽  
Vaughan T. Lewis ◽  
Ashley P. Smith ◽  
Andrew C. Singer
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Training Data ◽  
Regulatory Oversight ◽  
Untreated Wastewater ◽  
Untreated Sewage ◽  
Water Companies ◽  
Analytical Approaches ◽  
Sewage Discharges

AbstractMonitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of ‘spill’ and ‘no-spill’ effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a ‘spill’. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

Effect of the Implementation of a Surgical Care Bundle in the Incidence of Surgical Site Infection in Spine Surgery

Spine ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Gema Vicente-Sánchez ◽  
Marcos Alonso-García ◽  
Ana Isabel Hijas-Gómez ◽  
Rafael González-Díaz ◽  
Javier Martínez-Martín ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Spine Surgery ◽  
Surgical Care ◽  
Care Bundle ◽  
Site Infection

Association of Medicaid expansion under the Affordable Care Act with access to elective spine surgical care

2021 ◽  
pp. 1-9
Author(s):  
Jacob K. Greenberg ◽  
Derek S. Brown ◽  
Margaret A. Olsen ◽  
Wilson Z. Ray
Keyword(s):  
United States ◽  
Spine Surgery ◽  
Surgical Procedures ◽  
Affordable Care Act ◽  
Surgical Care ◽  
The United States ◽  
Medicaid Expansion ◽  
Percentage Points ◽  
The Affordable Care Act ◽  
The Impact

OBJECTIVE The Affordable Care Act expanded Medicaid eligibility in many states, improving access to some forms of elective healthcare in the United States. Whether this effort increased access to elective spine surgical care is unknown. This study’s objective was to evaluate the impact of Medicaid expansion under the Affordable Care Act on the volume and payer mix of elective spine surgery in the United States. METHODS This study evaluated elective spine surgical procedures performed from 2011 to 2016 and included in the all-payer State Inpatient Databases of 10 states that expanded Medicaid access in 2014, as well as 4 states that did not expand Medicaid access. Adult patients aged 18–64 years who underwent elective spine surgery were included. The authors used a quasi-experimental difference-in-difference design to evaluate the impact of Medicaid expansion on hospital procedure volume and payer mix, independent of time-dependent trends. Subgroup analysis was conducted that stratified results according to cervical fusion, thoracolumbar fusion, and noninstrumented surgery. RESULTS The authors identified 218,648 surgical procedures performed in 10 Medicaid expansion states and 118,693 procedures performed in 4 nonexpansion states. Medicaid expansion was associated with a 17% (95% CI 2%–35%, p = 0.03) increase in mean hospital spine surgical volume and a 23% (95% CI −0.3% to 52%, p = 0.054) increase in Medicaid volume. Privately insured surgical volumes did not change significantly (incidence rate ratio 1.13, 95% CI −5% to 34%, p = 0.18). The increase in Medicaid volume led to a shift in payer mix, with the proportion of Medicaid patients increasing by 6.0 percentage points (95% CI 4.1–7.0, p < 0.001) and the proportion of private payers decreasing by 6.7 percentage points (95% CI 4.5–8.8, p < 0.001). Although the magnitude of effects varied, these trends were similar across procedure subgroups. CONCLUSIONS Medicaid expansion under the Affordable Care Act was associated with an economically and statistically significant increase in spine surgery volume and the proportion of surgical patients with Medicaid insurance, indicating improved access to care.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

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