Today’s health systems face two major, interrelated challenges: employee burnout and staffing shortages. Nearly 50% of surveyed physicians and nurses have reported substantial burnout symptoms due to the heavy burden of bureaucratic and administrative tasks and working too many hours. As a result, many workers have chosen to walk away from the industry in search of better work-life balance, leaving hospitals with large staffing gaps they’re unable to fill. More than half of US hospitals report nurse vacancy rates above 7.5%, and overtime and agency spend has increased 169% since 2013. Unfortunately, many estimates suggest the healthcare worker shortage will only get worse over the coming decade.
To face both these challenges, providers must continue to optimize their staffing models in ways that prioritize health practitioner well-being while ensuring the best possible patient experience and outcomes. Data platforms will play a critical role by giving providers central access to data from disparate systems and advanced analytics and machine learning models they can use to forecast staffing needs more accurately. With these insights, health organizations can better balance caseloads and help ensure adequate staffing at all times to prevent burnout and improve patient care.
While clinical data can tell practitioners a great deal about their patients, operational systems, such as human capital management (HCM) systems, can tell care organizations a great deal about their employees, providing information such as historical schedules, hours worked, and sick time taken by clinicians and other staff. As the following architecture demonstrates, Oracle Data Platform unifies clinical and operational data and uses advanced analytics and machine learning to help providers understand how staffing models impact patient outcomes, how staffing decisions may impact the next week of care in near real time, what staffing gaps may need to be filled in the event of another major surge in COVID-19 cases, what an optimal staffing model looks like for any given point in time, and more.
There are three main ways to inject data into an architecture to enable healthcare organizations to understand how to best staff each department at any given point in time.
Data persistence and processing options for all the collected data are built on four components.
The ability to analyze, predict, and act relies on two technologies.
Beyond providing your healthcare organization with the ability to develop better, more accurate staffing models, Oracle Data Platform can also help you optimize operations in other areas to improve patient care, lower costs, and elevate the employee experience. Here are some examples.
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此研討會中的實驗室涵蓋 Oracle Cloud Infrastructure (OCI) 核心服務介紹，包括虛擬雲端網路 (VCN) 以及運算和儲存服務。立即開始 OCI 核心服務實驗
在此研討會中，您將會瞭解使用 Oracle Autonomous Database 的步驟。立即開始 Autonomous Database 快速入門實驗
本實驗將引導您將試算表上傳到 Oracle Database 表中，然後根據此新表格建立應用模組。立即開始此實驗
在本實驗中，您將會在 Oracle Cloud Infrastructure (OCI) 中的兩個運算執行處理上部署 Web 伺服器，並使用負載平衡器以高可用性模式 (High Availability) 進行設定。立即開始 HA 應用程式實驗
瞭解架構師與其他客戶如何部署各種工作負載，從企業應用程式至 HPC，以及從微服務到資料湖。透過「Built & Deployed」影片系列，瞭解最佳實務，聽聽其他客戶架構師的分享，您也可使用我們的「按一下即可部署」功能來部署許多工作負載，或者從我們的 GitHub 存放區自行部署。
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