logo

DoD FY15 Metrics: Transitioning Training to Reality (RealMETRX) Award

Sector: Advanced Electronics • Location: United States of America

Source: Grants.gov

Project
Archived

The FY16 JPC-1/MSIS RealMETRX is seeking research to determine, define, and validate the best indicators (metrics/evaluation criteria) of training proficiency that are amenable to appraisal using medical simulation systems and are empirically linked to optimal provision of patient care. What are some of the best metrics and evaluation criteria to measure effective decision making of novice or even

Project Information FAQ

Project Information

5 Q
The project “DoD FY15 Metrics: Transitioning Training to Reality (RealMETRX) Award” is an infrastructure initiative in the Advanced Electronics sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

Want to explore the full details? View the full report

Participants

Sponsoring Agency

Obfuscated Data

Company

Obfuscated Data

Status

Original status

archived

Taiyo status

Obfuscated Data

Taiyo last update

00-00-0000

Available timestamps

00-00-0000

Available timestamp type

Obfuscated Data

Contact

Contact name

Obfuscated Data

Phone

0000000000

Email

ObfuscatedData@email.com

Address

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Description

Description

The FY16 JPC-1/MSIS RealMETRX is seeking research to determine, define, and validate the best indicators (metrics/evaluation criteria) of training proficiency that are amenable to appraisal using medical simulation systems and are empirically linked to optimal provision of patient care. What are some of the best metrics and evaluation criteria to measure effective decision making of novice or even not-so novice healthcare personnel to better measure the multitude of variables and patient outcome contributors that could occur from the first healthcare encounter, to the time of discharge and even near-term follow-up (such as within the first 6 months)? What are the best metrics/evaluation criteria that could be used to (1) accelerate acquisition of maturity and experience level for novice and not-so novice healthcare personnel and (2) compare them to similar high-performing colleagues considered to be experienced within their discipline? It is expected that award recipients will use statistical approaches to determine the best metrics and evaluation criteria that will objectively assess and measure the transition from training using medical simulation systems to that of actual medical practice. It is expected that award recipients will concentrate their research within acute trauma care, critical care, and prolonged care. It is expected that the award recipients will consider healthcare scenarios and medical conditions in order to uncover common patient outcomes or training-sensitive outcome indicators versus those that are currently used to evaluate tasks, skills, and procedures. Metrics produced should include as many aspects of the continuum of care as possible and should focus on acute trauma care, critical care, and prolonged care. Military-relevant injuries and conditions should be considered, but should not constitute the entirety of the variables, metrics, and evaluation criteria. It is anticipated that many of these variables, metrics, and evaluation criteria will transcend across the military, Veterans Health Administration, academic, inpatient, outpatient clinics, rural healthcare settings, private and public hospitals, and international healthcare situations.

Original sub-sector

Obfuscated

Original Currency

USD

Original budget

000000000000000

Procurement method

Obfuscated Data

Budget

000000000000000

Location

Region

Obfuscated

Country

Obfuscated

State

Obfuscated Data

County

Obfuscated

Location

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source

Source reliability

High

Data quality score

100%

Source

Obfuscated Data

URL

obfuscated_data,obfuscateddata.com

More Details

Project Type

Obfuscated Data

Article Published Date

Obfuscated Data