Clinical Interactions in Electronic Medical Records Towards the Development of a Token-Economy Model

By November 2015, the West Africa Ebola epidemic had caused 28598 infections and 11299 deaths in the three countries most affected. The outbreak required rapid innovation and adaptation. Médecins sans Frontières (MSF) scaled up its usual 20-30 bed Ebola management centres (EMCs) to 100-300 beds with over 300 workers in some settings. This brought challenges in patient and clinical data management resulting from the difficulties of working safely with high numbers of Ebola patients. We describe a project MSF established with software developers and the Google Social Impact Team to develop context-adapted tools to address the challenges of recording Ebola clinical information. We share the outcomes and key lessons learned in innovating rapidly under pressure in difficult environmental conditions. Information on adoption, maintenance, and data quality was gathered through review of project documentation, discussions with field staff and key project stakeholders, and analysis of tablet data. In March 2015, a full prototype was deployed in Magburaka EMC, Sierra Leone. Inpatient data were captured on 204 clinical interactions with 34 patients from 5 March until 10 April 2015. Data continued to also be recorded on paper charts, creating theoretically identical record “pairs” on paper and tablet. 85 record pairs for 32 patients with 26 data items (temperature and symptoms) per pair were analysed. The average agreement between sources was 85%, ranging from 69% to 95% for individual variables. The time taken to deliver the product was more than that anticipated by MSF (7 months versus 6 weeks). Deployment of the tablet coincided with a dramatic drop in patient numbers and thus had little impact on patient care. We have identified lessons specific to humanitarian-technology collaborative projects and propose a framework for emergency humanitarian innovation. Time and effort is required to bridge differences in organisational culture between the technology and humanitarian worlds. This investment is essential for establishing a shared vision on deliverables, urgency, and ownership of product.

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Electronic medical records in humanitarian emergencies – the development of an Ebola clinical information and patient management system

F1000Research ◽

10.12688/f1000research.8287.3 ◽

2017 ◽

Vol 5 ◽

pp. 1477 ◽

Cited By ~ 3

Author(s):

Kiran Jobanputra ◽

Jane Greig ◽

Ganesh Shankar ◽

Eric Perakslis ◽

Ronald Kremer ◽

...

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Social Impact ◽

Clinical Information ◽

Shared Vision ◽

Lessons Learned ◽

Software Developers ◽

Humanitarian Emergencies ◽

Clinical Interactions ◽

Good Agreement

By November 2015, the West Africa Ebola epidemic had caused 28598 infections and 11299 deaths in the three countries most affected. The outbreak required rapid innovation and adaptation. Médecins sans Frontières (MSF) scaled up its usual 20-30 bed Ebola management centres (EMCs) to 100-300 beds with over 300 workers in some settings. This brought challenges in patient and clinical data management resulting from the difficulties of working safely with high numbers of Ebola patients. We describe a project MSF established with software developers and the Google Social Impact Team to develop context-adapted tools to address the challenges of recording Ebola clinical information. We share the outcomes and key lessons learned in innovating rapidly under pressure in difficult environmental conditions. Information on adoption, maintenance, and data quality was gathered through review of project documentation, discussions with field staff and key project stakeholders, and analysis of tablet data. In March 2015, a full prototype was deployed in Magburaka EMC, Sierra Leone. Inpatient data were captured on 204 clinical interactions with 34 patients from 5 March until 10 April 2015. Data continued to also be recorded on paper charts, creating theoretically identical record “pairs” on paper and tablet. 83 record pairs for 33 patients with 22 data items (temperature and symptoms) per pair were analysed. The overall Kappa coefficient for agreement between sources was 0.62, but reduced to 0.59 when rare bleeding symptoms were excluded, indicating moderate to good agreement. The time taken to deliver the product was more than that anticipated by MSF (7 months versus 6 weeks). Deployment of the tablet coincided with a dramatic drop in patient numbers and thus had little impact on patient care. We have identified lessons specific to humanitarian-technology collaborative projects and propose a framework for emergency humanitarian innovation. Time and effort is required to bridge differences in organisational culture between the technology and humanitarian worlds. This investment is essential for establishing a shared vision on deliverables, urgency, and ownership of product.

Download Full-text

Electronic medical records in humanitarian emergencies – the development of an Ebola clinical information and patient management system

F1000Research ◽

10.12688/f1000research.8287.1 ◽

2016 ◽

Vol 5 ◽

pp. 1477 ◽

Cited By ~ 2

Author(s):

Kiran Jobanputra ◽

Jane Greig ◽

Ganesh Shankar ◽

Eric Perakslis ◽

Ronald Kremer ◽

...

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Social Impact ◽

Clinical Information ◽

Shared Vision ◽

Lessons Learned ◽

Software Developers ◽

Humanitarian Emergencies ◽

Individual Variables ◽

Clinical Interactions

By November 2015, the West Africa Ebola epidemic had caused 28598 infections and 11299 deaths in the three countries most affected. The outbreak required rapid innovation and adaptation. Médecins sans Frontières (MSF) scaled up its usual 20-30 bed Ebola management centres (EMCs) to 100-300 beds with over 300 workers in some settings. This brought challenges in patient and clinical data management resulting from the difficulties of working safely with high numbers of Ebola patients. We describe a project MSF established with software developers and the Google Social Impact Team to develop context-adapted tools to address the challenges of recording Ebola clinical information. We share the outcomes and key lessons learned in innovating rapidly under pressure in difficult environmental conditions. Information on adoption, maintenance, and data quality was gathered through review of project documentation, discussions with field staff and key project stakeholders, and analysis of tablet data. In March 2015, a full prototype was deployed in Magburaka EMC, Sierra Leone. Inpatient data were captured on 204 clinical interactions with 34 patients from 5 March until 10 April 2015. 85 record “pairs” for 32 patients with 26 data items (temperature and symptoms) per pair were analysed. The average agreement between sources was 85%, ranging from 69% to 95% for individual variables. The time taken to deliver the product was more than that anticipated by MSF (7 months versus 6 weeks). Deployment of the tablet coincided with a dramatic drop in patient numbers and thus had little impact on patient care. We have identified lessons specific to humanitarian-technology collaborative projects and propose a framework for emergency humanitarian innovation. Time and effort is required to bridge differences in organisational culture between the technology and humanitarian worlds. This investment is essential for establishing a shared vision on deliverables, urgency, and ownership of product.

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Adolescent Substance Use Screening Integrated With Electronic Medical Records

PsycEXTRA Dataset ◽

10.1037/e520552015-006 ◽

2014 ◽

Author(s):

C. McKenna ◽

B. Gaines ◽

C. Hatfield ◽

S. Helman ◽

L. Meyer ◽

...

Keyword(s):

Substance Use ◽

Electronic Medical Records ◽

Medical Records ◽

Adolescent Substance Use ◽

Adolescent Substance

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Technology and health care: Efficiency, frustration, and disconnect in the transition to electronic medical records

PsycEXTRA Dataset ◽

10.1037/e639892013-007 ◽

2013 ◽

Author(s):

Kate Magsamen-Conrad ◽

Maria Checton

Keyword(s):

Health Care ◽

Electronic Medical Records ◽

Medical Records ◽

Health Care Efficiency

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A Digital Economy Model for Talent Prediction Data Analytic

ICONIET PROCEEDING ◽

10.33555/iconiet.v2i4.44 ◽

2019 ◽

Vol 2 (4) ◽

pp. 260-266

Author(s):

Haru Purnomo Ipung ◽

Amin Soetomo

Keyword(s):

Business Model ◽

Industrial Revolution ◽

Technology Innovation ◽

Cost Effective ◽

Sharing Economy ◽

Token Economy ◽

Economy Model ◽

New Business ◽

The Cost ◽

Data Analytic

This research proposed a model to assist the design of the associated data architecture and data analytic to support talent forecast in the current accelerating changes in economy, industry and business change due to the accelerating pace of technological change. The emerging and re-emerging economy model were available, such as Industrial revolution 4.0, platform economy, sharing economy and token economy. Those were driven by new business model and technology innovation. An increase capability of technology to automate more jobs will cause a shift in talent pool and workforce. New business model emerge as the availabilityand the cost effective emerging technology, and as a result of emerging or re-emerging economic models. Both, new business model and technology innovation, create new jobs and works that have not been existed decades ago. The future workers will be faced by jobs that may not exist today. A dynamics model of inter-correlation of economy, industry, business model and talent forecast were proposed. A collection of literature review were conducted to initially validate the model.

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