+1 Recommend
2 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Predictive Modeling for Telemedicine Service Demand

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Emergency teleneurology care has grown in magnitude, impact, and validation. Stroke is a leading cause of death in the United States, and timely treatment of stroke results in better outcomes for patients. Teleneurology provides evidence-based care to patients even when a board-certified neurologist is not physically on site. Determining staffing demand for telemedicine consultation for a specific period of time is an integral part of the decision-making activities of providers of acute care telemedicine services. This study aims to build a forecasting model to predict consultation demand to optimize telemedicine provider staffing. Such forecasting models acquire added importance in emergency situations such as the current COVID-19 pandemic.

          Materials and methods

          This study trained consultation data of SOC Telemed, a private telemedicine provider, from 411 hospitals nationwide and involving 97,593 incidents of consultations. The forecasting model analyzes multiple characteristics, including hospital size (number of beds), annual volume, patient demographics, time of consultation, and reason for consultation.


          Several regression techniques were used to demonstrate a strong correlation between these features and weekly demand with R 2 = 0.7821. Reason for consultation in the past week was the strongest predictor for the demand in the next week with R 2 = 0.7899.


          A predictive model for demand forecasting can optimize telemedicine resources to improve patient care and help telemedicine providers decide how many physicians to staff. The goal of the forecasting model is to improve patient care and outcomes by providing physicians timely and efficiently to meet the consultation demand. The ability to predict demand and calculate expected volume allows telemedicine providers to schedule physicians in advance. This mitigates the clinical risk of excess patient demand and long waiting time, as well as the financial risk of scheduling a surplus of physicians.

          Related collections

          Most cited references 61

          • Record: found
          • Abstract: found
          • Article: not found

          Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke.

          Intravenous thrombolysis with alteplase is the only approved treatment for acute ischemic stroke, but its efficacy and safety when administered more than 3 hours after the onset of symptoms have not been established. We tested the efficacy and safety of alteplase administered between 3 and 4.5 hours after the onset of a stroke. After exclusion of patients with a brain hemorrhage or major infarction, as detected on a computed tomographic scan, we randomly assigned patients with acute ischemic stroke in a 1:1 double-blind fashion to receive treatment with intravenous alteplase (0.9 mg per kilogram of body weight) or placebo. The primary end point was disability at 90 days, dichotomized as a favorable outcome (a score of 0 or 1 on the modified Rankin scale, which has a range of 0 to 6, with 0 indicating no symptoms at all and 6 indicating death) or an unfavorable outcome (a score of 2 to 6 on the modified Rankin scale). The secondary end point was a global outcome analysis of four neurologic and disability scores combined. Safety end points included death, symptomatic intracranial hemorrhage, and other serious adverse events. We enrolled a total of 821 patients in the study and randomly assigned 418 to the alteplase group and 403 to the placebo group. The median time for the administration of alteplase was 3 hours 59 minutes. More patients had a favorable outcome with alteplase than with placebo (52.4% vs. 45.2%; odds ratio, 1.34; 95% confidence interval [CI], 1.02 to 1.76; P=0.04). In the global analysis, the outcome was also improved with alteplase as compared with placebo (odds ratio, 1.28; 95% CI, 1.00 to 1.65; P<0.05). The incidence of intracranial hemorrhage was higher with alteplase than with placebo (for any intracranial hemorrhage, 27.0% vs. 17.6%; P=0.001; for symptomatic intracranial hemorrhage, 2.4% vs. 0.2%; P=0.008). Mortality did not differ significantly between the alteplase and placebo groups (7.7% and 8.4%, respectively; P=0.68). There was no significant difference in the rate of other serious adverse events. As compared with placebo, intravenous alteplase administered between 3 and 4.5 hours after the onset of symptoms significantly improved clinical outcomes in patients with acute ischemic stroke; alteplase was more frequently associated with symptomatic intracranial hemorrhage. (ClinicalTrials.gov number, NCT00153036.) 2008 Massachusetts Medical Society
            • Record: found
            • Abstract: not found
            • Article: not found

            Executive Summary: Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association

              • Record: found
              • Abstract: found
              • Article: not found

              Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association.

              The authors present an overview of the current evidence and management recommendations for evaluation and treatment of adults with acute ischemic stroke. The intended audiences are prehospital care providers, physicians, allied health professionals, and hospital administrators responsible for the care of acute ischemic stroke patients within the first 48 hours from stroke onset. These guidelines supersede the prior 2007 guidelines and 2009 updates. Members of the writing committee were appointed by the American Stroke Association Stroke Council's Scientific Statement Oversight Committee, representing various areas of medical expertise. Strict adherence to the American Heart Association conflict of interest policy was maintained throughout the consensus process. Panel members were assigned topics relevant to their areas of expertise, reviewed the stroke literature with emphasis on publications since the prior guidelines, and drafted recommendations in accordance with the American Heart Association Stroke Council's Level of Evidence grading algorithm. The goal of these guidelines is to limit the morbidity and mortality associated with stroke. The guidelines support the overarching concept of stroke systems of care and detail aspects of stroke care from patient recognition; emergency medical services activation, transport, and triage; through the initial hours in the emergency department and stroke unit. The guideline discusses early stroke evaluation and general medical care, as well as ischemic stroke, specific interventions such as reperfusion strategies, and general physiological optimization for cerebral resuscitation. Because many of the recommendations are based on limited data, additional research on treatment of acute ischemic stroke remains urgently needed.

                Author and article information

                Telehealth and Medicine Today
                Partners in Digital Health
                07 May 2020
                : 5
                [1 ]Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
                [2 ]Biological Sciences Department, Wellesley College, Wellesley, MA, USA
                [3 ]SOC Telemed, Reston, VA, USA
                [4 ]Institute for Medical Engineering and Science & Computer Science, Artificial Intelligence Lab, and Electrical Engineering and Computer Science Department, MIT, Cambridge, MA, USA
                Author notes
                Corresponding Author: Agni Kumar, Massachusetts Institute of Technology, Departments of EECS, Mathematics, Email: agnik@ 123456mit.edu
                © 2020 Agni Kumar

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

                Original Clinical Research


                Comment on this article