Developing a Robust Strategy Map in Balanced Scorecard Model Using Scenario Planning

The key to successful strategy implementation in an organization is for people in the organization to understand it, which requires the establishment of complicated but vital processes whereby the intangible assets are converted into tangible outputs. In this regard, a strategy map is a useful tool that helps execute this difficult task. However, such maps are typically developed based on ambiguous cause-effect relationships that result from the extrapolation of past data and flawed links with possible futures. However, if the strategy map is a mere reflection of the status quo but not future conditions and does not embrace real-world uncertainties, it will endanger the organization since it posits that the current situation will continue. In order to compensate for this deficiency, the environmental scenarios affecting an organization were identified in the present study. Then the strategy map was developed in the form of a scenario-based balanced scorecard. Besides, the effect of environmental changes on the components of the strategy map was investigated using the strategy maps illustrated over time together with the corresponding cash flow vectors. Subsequently, a method was proposed to calculate the degree of robustness of every component of the strategy map for the contingency of every scenario. Finally, the results were applied to a post office

Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic.

BACKGROUND: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and increased risk of death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, the need for intensive care (admission to the Intensive Care Unit; ICU) as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. PURPOSE: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to predict adverse outcomes following ED admission. MATERIALS AND METHODS: Light Gradient Boosting Machine (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using the 15 most important variables to increase applicability of the models in clinical settings. To predict risk (or early stratified risk) of the aforementioned health outcome events, transfer learning from the CheXNet model was also implemented on the available data. This research utilized clinical data and chest radiographs of 3,571 patients, 18 years and older, admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. MAIN FINDINGS: The research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)), predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), risk stratification of the need for ICU admission (AUC = 0.675 (0.620-0.713)) and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data. PRINCIPAL CONCLUSIONS: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms