@article{651232,

title = {Patients’ Perceptions of Barriers and Facilitators to the Adoption of e-Hospitals: Cross-sectional Study in Western China},

author = {Peiyi Li and Yunmei Luo and Xuexin Yu and Jin Wen and Elizabeth Mason and Weimin Li and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/ehospitals_adoption.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Journal of Medical Internet Research},

volume = {22},

number = {6},

pages = {e17221},

abstract = {Background: As an innovative approach to providing web-based health care services from physical hospitals to patients at a distance, e-hospitals (ie, extended care hospitals through the internet) have been extensively developed in China. This closed health care delivery chain was developed by combining e-hospitals with physical hospitals; treatment begins with web-based consultation and registration, and then, patients are diagnosed and treated in a physical hospital. This approach is promising in its ability to improve accessibility, efficiency, and quality of health care. However, there is limited research on end userstextquoteright acceptance of e-hospitals and the effectiveness of strategies aimed to prompt the adoption of e-hospitals in China. 

 

 

 

	Objective: This study aimed to provide insights regarding the adoption of e-hospitals by investigating patientstextquoteright willingness to use e-hospitals and analyzing the barriers and facilitators to the adoption of this technology. 

 

 

 

	Methods: We used a pretested self-administered questionnaire and performed a cross-sectional analysis in 1032 patients across three hierarchical hospitals in West China from June to August 2019. Patientstextquoteright sociodemographic characteristics, medical history, current disease status, proficiency with electronic devices, previous experience with web-based health services, willingness to use e-hospitals, and perceived facilitators and barriers were surveyed. Multiple significance tests were employed to examine disparities across four age groups, as well as those between patients who were willing to use e-hospitals and those who were not. Multivariate logistic regression was also performed to identify the potential predictors of willingness to use e-hospitals. 

 

 

 Results: Overall, it was found that 65.6% (677/1032) of participants were willing to use e-hospitals. The significant predictors of willingness to use e-hospitals were employment status (P=.02), living with children (P<.001), education level (P=.046), information technology skills (P<.001), and prior experience with web-based health care services (P<.001), whereas age, income, medical insurance, and familiarity with e-hospitals were not predictors. Additionally, the prominent facilitators of e-hospitals were convenience (641/677, 94.7%) and accessibility to skilled medical experts (489/677, 72.2%). The most frequently perceived barrier varied among age groups; seniors most often reported their inability to operate technological devices as a barrier (144/166, 86.7%), whereas young participants most often reported that they avoided e-hospital services because they were accustomed to face-to-face consultation (39/52, 75%). 

 

 

 

	Conclusions: We identified the variables, facilitators, and barriers that play essential roles in the adoption of e-hospitals. Based on our findings, we suggest that efforts to increase the adoption of e-hospitals should focus on making target populations accustomed to web-based health care services while maximizing ease of use and providing assistance for technological inquiries.},

keywords = {Adoption dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{647807,

title = {Adoption of new medical technologies: The effects of insurance coverage vs continuing medical education},

author = {Amir T Namin and Vahab Vahdat and Catherine DiGennaro and Roham Amid and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/adoption_insurance_vs_education.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Health Policy and Technology},

volume = {9},

number = {1},

pages = {31-41},

abstract = {Medical technologies innovate rapidly and responsively to patient needs, but the adoption of the latest technologies in practice can be delayed by lack of knowledge and ability to pay. Customized individually made (CIM) knee implants potentially provide an option for individuals to maintain moderate to high activity levels with fewer surgical revisions following a total knee replacement, however they are costlier upfront. Not only is the technology more expensive, but insurance typically covers around 50% (versus 90% for older off-the-shelf knee implants). We used a recent simulation model and analyzed the effects on overall adoption of CIM through 2026 and found that continuing medical education (CME)—a common intervention to increase the adoption of new medical technologies through increasing practitioner knowledge and comfort with the new technologies—can increase the adoption of CIM to 48% in the short term, but increasing insurance coverage to be equal to OTS knee replacement coverage increases the adoption to 87% in the sustained long term. Efforts to implement CME are well-placed and will increase the rate of adoption, however the combination of CME and increased insurance coverage provides the most benefit, with the technology reaching 80% of the population undergoing total knee replacement by 2021.},

keywords = {Adoption dynamics, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{651958,

title = {How Telemedicine Integrated into China’s Anti-COVID-19 Strategies: Case from a National Referral Center},

author = {Peiyi Li and Xiaoyu Liu and Elizabeth Mason and Guangyu Hu and Yongzhao Zhou and Weimin Li and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/telemedicine_china.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {BMJ Health & Care Informatics},

number = {27},

pages = {e100164},

abstract = {Introduction We present the integration of telemedicine into the healthcare system of West China Hospital of Sichuan University (WCH), one of the largest hospitals in the world with 4300 inpatient beds, as a means for maximising the efficiency of healthcare delivery during the COVID-19 pandemic. 

 

 

 

	Methods Implemented on 22 January 2020, the telemedicine technology allowed WCH providers to conduct teleconsultations, telerounds, teleradiology and tele-intensive care unit, which in culmination provided screening, triage and treatment for COVID-19 and other illnesses. To encourage its adoption, the government and the hospital publicised the platform on social media and waived fees. 

 

 

 

	Discussion From 1 February to 1 April 2020, 10557 online COVID-19 consultations were conducted for 6662 individuals; meanwhile, 32676 patients without COVID completed virtual follow-ups. We discuss that high-quality, secure, affordable and user-friendly telemedical platforms should be integrated into global healthcare systems to help decrease the transmission of the virus and protect healthcare providers from infection. 

 

 

 

	 },

keywords = {Adoption dynamics, Infectious diseases},

pubstate = {published},

tppubtype = {article}

}

@article{631218,

title = {The Internet of Things Promises New Benefits and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products},

author = {Mohammad S. Jalali and Jessica P Kaiser and Michael Siegel and Stuart Madnick},

url = {https://scholar.harvard.edu/files/jalali/files/adoption_dynamics_of_iot_products.pdf},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {IEEE Security and privacy},

volume = {17},

number = {2},

pages = {39-48},

keywords = {Adoption dynamics, Cybersecurity, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631232,

title = {The Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants},

author = {Amir T Namin and Mohammad S. Jalali and Vahab Vahdat and Hany S Bedair and Mary I O'Connor and Sagar Kamarthi and Jacqueline A Isaacs},

url = {https://scholar.harvard.edu/files/jalali/files/theadoptionofnewmedicaltechnologies.pdf

https://scholar.harvard.edu/files/jalali/files/supporting_information.pdf

https://scholar.harvard.edu/files/jalali/files/modeling_documentation_and_instruction_for_reproducibility_mdir.pdf},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Value in Health},

volume = {22},

number = {4},

pages = {423-430},

abstract = {Objectives: To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants, and to compare patient outcomes and cost-effectiveness of off-the-shelf (OTS) and CIM implants.Study Design: A system dynamics simulation model is developed to study adoption dynamics of CIM and meet the research objectives.Methods: The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then, the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within three years of primary knee replacement implants were used as performance metrics.Results: The simulation results indicate that, by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time, and cut down cumulative healthcare costs by approximately $38 billion.Conclusions: CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents a first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be utilized to consider the adoption of any emerging customized therapies for personalized medicine.},

keywords = {Adoption dynamics, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631231,

title = {Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative},

author = {Mohammad S. Jalali and Hazhir Rahmandad and Sally Lawrence Bullock and Seung Hee Lee-Kwan and Joel Gittelsohn and Alice Ammerman},

url = {https://scholar.harvard.edu/files/jalali/files/dynamics_of_intervention_adoption.pdf, dynamics of intervention adoption.pdf

https://scholar.harvard.edu/files/jalali/files/vensimfiles.zip, Vensim Files},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Social Science & Medicine},

volume = {224},

pages = {67-76},

abstract = {Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.},

keywords = {Adoption dynamics, Obesity, Participatory modeling, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631210,

title = {Information diffusion through social networks: The case of an online petition},

author = {Mohammad S. Jalali and Armin Ashouri and Oscar Herrera-Restrepo and Hui Zhang},

url = {https://scholar.harvard.edu/files/jalali/files/information_diffusion.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Expert Systems with Applications},

volume = {44},

pages = {187–197},

abstract = {People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers. Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have textquoteleftdisproportionatetextquoteright effects in increasing the total number of signatures. The model is fully documented for further development and replications.},

keywords = {Adoption dynamics},

pubstate = {published},

tppubtype = {article}

}