Online citations, reference lists, and bibliographies.

Predictive Modeling For Telemedicine Service Demand

Agni Kumar, Nancy Hung, Yuhan Wu, Robyn Baek, Amar Gupta
Published 2020 · Medicine
Cite This
Download PDF
Analyze on Scholarcy
Share
Introduction: Emergency teleneurology care has grown in magnitude, impact, and validation. Stroke is a leading cause of death in the US and the 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 characteristics including hospital size (number of beds), annual volume, patient demographics, time of consultation, and reason for consultation. Results: Several regression techniques were used to demonstrate a strong correlation between these features and weekly demand with r2 = 0.7821. Reason for consult in the past week was the strongest predictor for the demand in the next week with r2 = 0.7899. Conclusion: 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 consultation demand. The ability to predict demand and calculate expected volume allow 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 surplus of physicians.
This paper references
10.1016/j.jbi.2015.06.022
A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia
Patrick Aboagye-Sarfo (2015)
10.1016/j.ajem.2014.05.052
Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska.
Ozgur M. Araz (2014)
A review of physician-to-population ratios
Predicting inpatient length of stay in Western New York health service area using machine learning algorithms
H Salah (2017)
10.1177/1357633X15589534
The effects of telemedicine on racial and ethnic disparities in access to acute stroke care
Michael J. Lyerly (2016)
10.1161/01.STR.0000196957.55928.ab
Time Is Brain—Quantified
Jeffrey L. Saver (2006)
Safety and efficacy of thrombolysis in telestroke: A systematic review and meta-analysis
J Kepplinger (2016)
10.3414/ME17-01-0024
Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.
Xingyu Zhang (2017)
10.1051/IJMQE/2015011
Forecast emergency room visits – a major diagnostic categories based approach
Abdeljelil Aroua (2015)
10.1111/j.1553-2712.2008.00083.x
The challenge of predicting demand for emergency department services.
Melissa Lee McCarthy (2008)
Planning the future of emergency departments: Forecasting ED patient arrivals by using regression and neural network models
Muhammet Gul (2016)
10.1161/CIRCOUTCOMES.116.003242
Improving Door-to-Needle Times for Acute Ischemic Stroke: Effect of Rapid Patient Registration, Moving Directly to Computed Tomography, and Giving Alteplase at the Computed Tomography Scanner
N. Kamal (2017)
10.1155/2016/1382434
Presentations to Emergency Departments for COPD: A Time Series Analysis
Rhonda J. Rosychuk (2016)
10.1179/174313208X331608
Seasonal variation in the occurrence of stroke in Northern Greece: a 10 year study in 8204 patients
Asterios Karagiannis (2010)
10.1155/2016/3863268
Forecasting Daily Volume and Acuity of Patients in the Emergency Department
Rafael Calegari (2016)
10.1177/1357633X18774460
Door to needle time and functional outcome for mild ischemic stroke over telestroke
Jillian Harvey (2018)
10.30953/tmt.v5.158
Neurology eConsults at Penn State Health: What, Why, and How?
Aiesha Ahmed (2019)
10.1016/j.ienj.2013.08.001
Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis.
Jochen Bergs (2014)
10.2217/WHE.09.82
Gender Differences in Patients with Acute Ischemic Stroke
Valeria Caso (2010)
10.1080/20476965.2017.1390185
Modelling and forecasting daily surgical case volume using time series analysis
Nazanin Zinouri (2018)
10.1136/jnnp.2005.086595
Thrombolysis in patients older than 80 years with acute ischaemic stroke: Canadian Alteplase for Stroke Effectiveness Study
P. Sylaja (2006)
10.1016/j.ajem.2014.03.011
A retrospective analysis of the utility of an artificial neural network to predict ED volume.
Nathan Benjamin Menke (2014)
10.1111/j.1553-2712.2012.01359.x
Predicting emergency department volume using forecasting methods to create a "surge response" for noncrisis events.
Valerie J. Chase (2012)
10.1136/bmj.i1981
Sparse data bias: a problem hiding in plain sight
Sander Greenland (2016)
10.1017/CEM.2016.193
P017: Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes
Michael B. Butler (2016)
10.1001/jamaneurol.2014.3844
Advantages and limitations of teleneurology.
Lawrence R Wechsler (2015)
Forecasting the emergency department Page 13 of 14 Telehealth and Medicine Today® ISSN 2471-6960 https://doi.org/10.30953/tmt.v5.186 patients
M Afilal (2016)
10.1016/J.JVS.2018.04.007
2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association
William J Powers (2018)
10.4103/1947-2714.110430
Winter Cardiovascular Diseases Phenomenon
Auda Fares (2013)
10.4018/irmj.2008010101
Outsourcing in the Healthcare Industry: Information Technology, Intellectual Property, and Allied Aspects
Amar Gupta (2008)
Chapter 4 public policies and health systems and services
(2007)
Early Management of Acute Ischemic Stroke: Focus on IV tPA and Timely Reperfusion
V. Nguyen (2016)
10.1089/TMJ.2017.0248
Rate of Symptomatic Intracerebral Hemorrhage Related to Intravenous tPA Administered Over Telestroke Within 4.5-Hour Window.
Al KasabSami (2018)
10.1212/WNL.0b013e318294b1cf
Supply and demand analysis of the current and future US neurology workforce
Timothy M Dall (2013)
10.1177/1941874415578532
Absolute and Relative Contraindications to IV rt-PA for Acute Ischemic Stroke
Jennifer E. Fugate (2015)
10.1159/000432405
Short Door-to-Needle Times in Acute Ischemic Stroke and Prospective Identification of Its Delaying Factors
S. M. Van Schaik (2015)
CarolinaEast improving stroke care with teleNeurology
Soc Telemed
10.1177/1357633X15577531
Post t-PA transfer to hub improves outcome of moderate to severe ischemic stroke patients
Shadi Yaghi (2015)
10.7326/M15-0969
Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation
Heidi D. Nelson (2016)
10.1212/WNL.0b013e318234332d
The cost-effectiveness of telestroke in the treatment of acute ischemic stroke
R. Nelson (2011)
10.1002/qre.2095
A Hybrid Approach for Forecasting Patient Visits in Emergency Department
Qinneng Xu (2016)
10.1111/acem.12244
Generalizability of a simple approach for predicting hospital admission from an emergency department.
J. Peck (2013)
10.1177/1357633X14552389
Experience with scaling up the Victorian Stroke Telemedicine programme
Dominique A. Cadilhac (2014)
Predicting inpatient length of stay in Western New York health service area using machine learning algorithms. Binghamton, NY: State University of New York; 2017
H. Salah (2017)
10.1007/s12199-012-0294-6
An overview of health forecasting
Ireneous N. Soyiri (2012)
10.1161/STROKEAHA.109.547869
Relative Risks for Stroke by Age, Sex, and Population Based on Follow-Up of 18 European Populations in the MORGAM Project
Kjell Asplund (2009)
10.1177/1747493017706239
Canadian Stroke Best Practice Recommendations: Telestroke Best Practice Guidelines Update 2017
Dylan P V Blacquiere (2017)
10.1007/s10729-011-9155-x
Long term staff scheduling of physicians with different experience levels in hospitals using column generation
Jens O. Brunner (2011)
10.1089/tmj.2017.0248
Rate of Symptomatic Intracerebral Hemorrhage Related to Intravenous tPA Administered Over Telestroke Within 4.5-Hour Window.
Sami Al Kasab (2018)
10.1007/s10916-016-0527-0
Forecasting the Emergency Department Patients Flow
Mohamed Afilal (2016)
10.4172/2155-9562.1000117
Gender Differences in Patients with Intravenous Thrombolytic and Conservative Treatment for Acute Ischemic Stroke
E. Broussalis (2011)
10.3389/fneur.2012.00033
REACH MUSC: A Telemedicine Facilitated Network for Stroke: Initial Operational Experience
R. Adams (2012)
10.1136/neurintsurg-2015-011806
CODE FAST: a quality improvement initiative to reduce door-to-needle times
Leslie Busby (2015)
10.1258/jtt.2009.090102
Impact of a telemedicine system on acute stroke care in a community hospital
Angels Pedragosa (2009)
10.1542/peds.2016-2785
Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department
Yuval Barak-Corren (2017)
10.1136/bmjopen-2017-018628
Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan
Wang-Chuan Juang (2017)
10.1097/NRL.0b013e3181a4b79c
Telemedicine Versus Telephone for Remote Emergency Stroke Consultations: A Critically Appraised Topic
Dan J. Capampangan (2009)
Teleneurology applications: Report of the Telemedicine Work Group of the American Academy of Neurology. Neurology
LR Wechsler (2013)
10.1002/hpm.2771
A comprehensive modelling framework to forecast the demand for all hospital services.
Muhammed Ordu (2019)
10.1177/1357633X18805661
Ideal telestroke time targets: Telestroke-based treatment times in the United States stroke belt
Krishna Nalleballe (2018)
10.1159/000331473
Are Stroke Occurrence and Outcome Related to Weather Parameters? Results from a Population-Based Study in Northern Portugal
Rui Magalhães (2011)
10.1089/tmj.2017.0210
Telemedicine for Parkinson's Disease: Limited Engagement Between Local Clinicians and Remote Specialists.
Molly J. Elson (2018)
10.1056/NEJMoa0804656
Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke.
W. Hacke (2008)
10.1212/WNL.0b013e31823433aa
Telestroke, QALYs, and current health care policy
Steven H. Rudolph (2011)
Teleneurology applications: Report of the Telemedicine Work Group of the American Academy of Neurology
L R Wechsler (2013)
10.1161/CIR.0000000000000157
Executive Summary: Heart Disease and Stroke Statistics—2015 Update A Report From the American Heart Association
D. Mozaffarian (2015)
10.1089/tmj.2017.0006
American Telemedicine Association: Telestroke Guidelines.
B. Demaerschalk (2017)
10.1007/s10916-014-0107-0
Time Series Modelling and Forecasting of Emergency Department Overcrowding
Farid Kadri (2014)
10.1161/01.STR.0000177534.02969.e4
REACH: Clinical Feasibility of a Rural Telestroke Network
David C. Hess (2005)



Semantic Scholar Logo Some data provided by SemanticScholar