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    Big Data for Determining the Top Health Performers

    To determine the top health performers and top-performing hospitals, we must determine specific characteristics upon which we rank the performance of those hospitals.

    Repeated data analysis for the top-performing hospitals showed that they share common features.  COVID-19 pandemic did not appear to show much difference for those features.

    IBM was one of the leading organizations that took the decision 25 years ago to perform an annual ranking for the top 100 best performing hospitals through IBM Watson Health Analytics.

    IBM Watson Health qualitative study revealed that those hospitals share the following characteristics:

    1. Continuous improvement for cultural awareness:

    Everyone in the community should be aware of the quality guidelines and should understand that achieving optimum performance is the main target of the organization.  There must be a common understanding and agreement that quality assurance and improvement are iterative processes.

    Hiring regulations and protocols should be compatible with the concept of assuring a uniform performance culture.  Leadership openings must include a trial project and enough probation period.  Targets’ achievements, surveys, and feedback after the probation period should be coherent with the organization’s policy.

    2. Top-quality nursing services:

    Some different certifications and frameworks can ensure the quality of the nursing services offered in the healthcare facility.

    Magnet is one famous certificate program offered by the American Nurses Credentialing Center (ANCC).  This program entails 5 model components, they can be summarized as follows:

    1. Transformational Leadership.
    2. Structural Empowerment.
    3. Exemplary Professional Practice.
    4. New Knowledge, Innovation, & Improvements.
    5. Empirical Quality Results.

    The Baldrige Program is another program organized by the National Institute of Standards and Technology (NSIT) that focuses on performance excellence by adopting the Baldrige Excellence Framework.   It ensures that the participating healthcare organizations adopt a nursing excellence framework.

    Most of the top-10 performing hospitals appeared to follow either Magnet or the Baldridge program.

    3. Effective Leadership:

    The more top management commitment to the work and community, the more the organization appeared to be a top performer.  The staff and the top management sense towards their teams and the patients were important factors affecting the tendency to preserve a high-quality service.  Leaders’ inspiration and representation of an ideal ethical model was a particularly important factor for motivating the team to preserve the high-quality service.  Continuous monitoring and evaluation by pointing out the success and failure according to predefined measures was also an important motivating factor for the team.

    4. High-performing hospital board:

    Strict hospital board members are in a continuous monitoring and evaluation process. Patient statistics like mortality and morbidity should be of higher importance to the hospital board than financial statistics.

    5. Being a part of a health system:

    Most of the top-ten hospitals appeared to be part of health systems. The whole community was dedicated to common predefined goals and strategic plans. Still, there was a clear line of demarcation between working as a team dedicated to achieving a specific goal and between the feeling of each worker’s autonomy and responsibility towards his or her specific work.

    6. Applying recent trends of information technology for service improvement:

    Technological innovation did not appear to be a target itself, but the benefits beyond the use of this technology.  Therefore, Telehealth was among the most prominent information technology applications in the leading hospitals.  Telehealth can save time and money and fill the deficiency in the number of physicians, especially in rural areas.  Another important application was the use of artificial intelligence and predictive modeling in healthcare.

    7. Considering data as the main axis for the quality improvement process:

    Health records and surveys and different data sources recorded inside the hospital can be the reference and main sources for any statistics, business intelligence algorithms that can judge the quality improvement process.  Sound data management can enforce quality standards.

    Data management governance and strategies should show flexibility to cope with any new advances in the reporting needs and regulations.

    15 Top Health Systems

    In addition to ranking the top 100- performer hospital, IBM Watson Health also ranks the top 15 health systems.  The top 15 health systems were categorized according to their scale into large, medium, and small systems.

    The Watson Health 15 Top Health Systems scorecard results were based on different key measures of performance like:

    • Inpatient and extended care quality
    • Operational efficiency
    • Financial health
    • Patient experience

    The hospitals’ systems that showed the best performance had the following traits:

    • Low inpatient mortality and patient complications.
    • Low 30-day readmission rates.
    • Faster emergency care.
    • Lower aftercare expenses.
    • High ratings in patients’ experience assessment surveys.
    • Great compliance following influenza immunization protocols

    The top 15 health systems report sheet showed that if the performance standards applied by the winners were achieved in all US health systems, we could save 43000 lives more (in-hospital), 29000 more patients could be free from any complications, and in-patients would have 12% fewer infection rates.

    Performance improvement can save time for both the service provider and the patient.  If the winners’ standards to be applied to all US hospitals, patients can go home a half-day earlier and have less waiting time in the emergency room by 38 minutes per visit.

    IBM Watson Heath Analytics can show how important can accurate data and precise records are.  Different insights can be extracted from analyzing this data, that could lead to a great change in the governmental strategies on the local and the national level.

    As stated earlier, considering data as the main axis for the improvement process was a commonly shared concern among the top health performer organizations.

    Many data scientists care about data related to ranking and assessing the top performers across the US.  Maybe the permanents obstacle they face always is having clean standardized data without losing much time in the data extraction or wrangling process.

    The presence of an organization like John Snow Labs can save much time for those scientists and researchers.  John Snow catalog contains many datasets that contain previous data about ranking and performance assessment across the US.

    I can list for example 2 of those datasets:

    1. US Counties Health Ranks Data Information and Top Performers 2017-2022

    This dataset contains information related to the data sources and years used to calculate the US counties’ ranks and data for the top performers and US overall values. The dataset comes in addition to the main dataset, US Counties Ranks by Health Outcomes and Determinants.

    1. Inpatient Prospective Payment System Top 100 DRGS 2013

    The dataset provided here includes hospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2013. These DRGs represent more than 7 million discharges or 60 percent of total Medicare IPPS discharges.

    The reader can download a free sample from both datasets for healthcare for free.

    By analyzing this comprehensive dataset, Generative AI in Healthcare can uncover valuable trends in hospital performance and costs, empowering a Healthcare Chatbot to offer patients more informed guidance on treatment options and expected expenses.

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    Our additional expert:
    Mohamed joined John Snow Labs (JSL) in Feb. 2016 as Healthcare Researcher and Author. Other than having 20+ years of experience moving between different healthcare domains (management, training, curricula design, solution architecture, clinical, research, and data management), Mohamed has good experience in working with SQL, big data, machine learning, and Python. Before joining JSL, Mohamed had worked as a Healthcare Facility Manager in his own private practice. He has also worked as a data manager, training consultant, and eHealth Researcher in various companies/organizations in Egypt, Canada, and US (Remotely).
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