Contemporary Organization Evaluation: Steward Health Care

Contemporary Organization Evaluation
Student Name
Institutional Affiliation
Course
Professor
Date
Contemporary Organization Evaluation
This paper investigates Steward Health Care, and its response to automated machine learning, discusses the disruptive nature of the change initiative and how the organization responded to its dynamics, evaluates the change program strategies employed and determines their success levels, appraises how the change impacted stakeholders and their response to it, as well as the change implications on interdepartmental collaboration. The paper also included my opinion on how well the organization responded and prepared to the change, evaluation of the implemented strategies, necessary modifications, and recommendations on extra approaches to help the organization manage change.
The Organization and Change Initiative
Steward Health Care, the biggest private hospital operator within the United States (US), is a physician-led medical care services organization focused on offering high-quality care to settings where clients reside. Steward’s exceptional medical care service delivery framework is anchored on innovativeness, technology, and care coordination processes to guarantee better patient outcomes. Steward runs 36 community healthcare facilities within ten states. The organization employs roughly 37,000 individuals and is constantly recognized for safety and quality. The Steward network incorporates over 26 emergency care facilities, 42 preferred competent nursing centers, substantial behavioral medical facilities, more than 7,300 beds under management, as well as over 1.1 million protected persons via the organization’s managed care and medical assurance services (Berthiaume & Carroll, 2017, n.p.).
Technological opportunities to record increasingly enormous amounts of information concerning patients and their contexts transform the healthcare sector. Machine learning offers a novel way to pinpoint patterns and understand data, which empowers medical practitioners to move closer to patient-centered medicine. In that way, machine learning constitutes a disruptive technology creating opportunities that can be employed within healthcare (Orion Health, 2018, p.1). Steward Health Care uses machine learning opportunities to make critical decisions about patients and staff, reduce costs, and enhance patient experiences and outcomes.
How the Change has been Disruptive and Organization’s Response to the Change Dynamics
Sixty percent of healthcare facilities’ operations emanate from staffing, depicting a considerable chunk of the hospital’s budget. A typical healthcare organization staffing model is anchored on average volume and census for scheduling and planning purposes. Unfortunately, by operating against the static average, this implies that the healthcare organization is understaffed during patient volume peaks, and during patient volume valleys, the facility is overstaffed. This is increasingly inefficient and results typically in hospitals incurring more expenses for on-call personnel and overtime remuneration. Machine learning enables Steward Health Care to find a proactive approach to eliminate such inefficiencies. The disruptive technology allows Steward to find actional opportunities to be increasingly proactive within their approach towards forecasting hospital’s staffing and volumes more efficiently (DataRobot Inc., 2019, p.2). DataRobot’s automated machine learning disrupted Stewards twofold. First, it offered the instruments to assist the organization in manipulating their data, effectively designing and evaluating models from that data, and finally learning from the data. Second, machine learning facilitated sharing data science expertise and knowledge to assist Steward in addressing their resources and skills vacuum (DataRobot Inc., 2019, p.3).
Steward Health Care responded to machine learning dynamics by embracing digital innovation. Steward collaborated with the Insight Digital Innovation team to mitigate patient hospital stays at its hospitals’ network, optimize its healthcare staff, and enhance care coordination. Real-time data analytics played a critical role in the entire process. Steward collaborated with Insight to design the length-of-stay monitoring system. The healthcare organization used the length-of-stay monitoring system to identify and address essential time gaps within patient care. The new tracking system has a waterfall impact that went beyond mitigating patient stay lengths. Steward Health Care used cumulative data gathered and evaluated from every department and medical center to adapt its length-of-stay framework to staffing needs. In addition, Steward Health Care, in collaboration with Insight, designed a platform for intelligence as well as decision support that assisted the network to reexamine its approach to patient care and change the way it operates (Insight Editor, 2018, n.p.). Steward Health Care leveraged the cost savings and elasticity of the increasingly agile environment by embracing machine learning opportunities and strategically applied robust analytics to each facet of the entity, from prediction of staffing and volume to claims and billing (Insight Editor, 2018, n.p.). Therefore, Steward Health Care has been leading in employing technology to enhance healthcare delivery.
Strategies the Organized used in its Change Plan and level of Success Experienced
Notably, Steward’s DataRobot models are anchored on its patent-pending and proprietary Proactive Labor Management (PLM) dashboard, a SaaS platform operated on Microsoft Azure accessible to all 38 healthcare facilities in the Steward Health Care Network. The organization used the accuracy tracker embedded on its PLM dashboard to attain 95 percent accuracy. The development team simultaneously rolled out the dashboard and models and identified new patterns and tweaking. For example, specific departments – like the Intensive Care Unit (ICU) or psychiatric – at some healthcare facilities proved to be accurate co...