White Wolf 2

Identifying critical operational mistakes on the spot - Saving lives and money

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Executive summary

Each day, about 1 in 25 patients has hospital associated infections (HAI). 75,000 patients died in 2011. No too long ago, the Ebola virus has killed many volunteers, nurses and doctors. The root cause is due to the real-life complications that cannot be simulated during trainings. Similar problems are founded in tactical operations. Using precision 3D tracking, White Wolf 2 can help with:

  • Task tracking and Instant alerts
    Site is digitally mapped to form reference areas. Operators wear sensors on their hands. Based on pre-defined rules (e.g. patient bed area reached without arriving at hand-wash area first), alerts can be issued.
  • Post-event action playback
    Raw sensor movements can be logged to be played back and correlated on demand. Helping operators understand the contextual reasons behind operational mistakes.
  • Predicting threats and problems
    Machine learning models can be ran on collected data to find patterns of dangerous behaviors and prevention steps can be performed.

In 2020, infection surveillance market will grow from $231.08 million (2015) to $445.79 million. Tactical training market will grow from $10.31 billion (2016) to $12.67 billion (2021). Our ideal customers will be organizations working on epidemic relief missions, in which one small mistake may cost lives (Ebola). We also see our solution to be used in many hospitals, predicting HAI cases, identifying potential problems in workflows, or optimizing certain processes. Military special forces or police can use our solution to gain much more insights into their live training scenarios. Existing solutions include Atlas Medical, ICNET, RL solutions, TheraDoc, BD, Sentri7. White Wolf 2 is different from all of them because our solution is based on contextual live-action data performed by the operators, rather than Statistical data from lab results, pharmacies, patient databases, or device logs. On an average, detecting hospital associated infections can take from 4 to 8 days. Our solution can detect mistakes and provide alerts instantly. White Wolf 2 is also portable and much less dependent on existing IT infrastructure, making it more available to various regions around the world such as remote Ebola infected sites.

Founded by Tam Nguyen - a NC State graduate student, White Wolf 2 has the potential of reducing human casualties caused by HAI or by other critical operational mistakes. Software is 100% cloud based and hardware can be deployed globally.

Project Deliverables

1. Solution

White Wolf 2 solution

CORE FEATURES:

  • Task tracking and Instant alerts
    Site is pre-map with reference areas. Operators will wear sensors on their hands. Based on pre-defined rules (e.g. patient bed area reached before hand wash area), alerts can be issued.
  • Post-event action playback
    Raw sensor movements can be logged to be played back and correlated on demand. This is crucial for inspectors to understand the contextual reasons behind mistakes.
  • Predicting threats and problems
    Machine learning models can be ran on collected sensor data to find patterns of dangerous behaviors so prevention steps can be executed

BASIC STRUCTURE:

  • Existing precision 3D tracking tech by PoXyz
  • NodeJS dashboard based on real time simulated data, displaying PowerBI graphs and intelligence, list of nurses and their related tasks, calculated threat levels of patient rooms.
  • A simple mobile client for nurses to keep track of their tasks (android and iphone)
  • Simple Cloudant and Azure databases
  • Software is 100% cloud based with abilities to scale and integrated with powerful machine learning capabilities provided by IBM and MS
  • Agile method was used with tool chain of
    [IBM control interface + Github] --> [IBM Build] --> [Sauce labs/tests] --> [Deploy]

Our top competitors are Atlas Medical, ICNET, RL solutions, TheraDoc, BD, Sentri7.
White Wolf 2 is different from all of them because our solution is based on Contextual live-action data performed by the operators, rather than Statistical data from lab results, pharmacies, patient databases, or device logs. White Wolf 2 is also portable and much less dependent on existing IT infrastructure, making it more available to various regions around the world such as remote Ebola infected sites.

2. Feasibility

MARKET OUTLOOK:

  • Infection surveillance market will grow from $231.08 million (2015) to $445.79 million (2020)
  • A hospital pay 5 million per year (besides salaries) to maintain a force of 600 nurses
  • Tactical training market will grow from $10.31 billion (2016) to $12.67 billion (2021)

POTENTIAL IMPACTS:

  • Protect outbreak responders, allowing high resolution surveillance and instant alerts, limiting chances of disease spread by responders' mistakes
  • Detect and predict issues allowing instant remedial actions (rather than 4 to 8 days per current average). This will lead to reduced treatment time, save money, reduce nurses workoads, boost productivity
  • Optimize nurses' workflows by identifying overlapings, excessive travel time, etc. Establish contextual audit trails improving accountability.
  • Provide more insights into live tactical training scenarios, giving immediate feedbacks, allowing officers chances to make corrections and improvements on their tactical behaviors, potentially help avoiding casualties in real combats.

POTENTIAL RISKS

  • Market risk - manageable by early market studies, buying market research docs, picking a market of focus
  • Competitive risks - manageable by getting methods, softwares patented
  • Legal & Regulatory risk - manageable by patent research, conform to standards, expand to overseas
  • Tech & operation risk - manageable by secure software eng, enough testing, strong backup plans
  • Financial risk - manageable by trying to secure contracts as soon as possible, carefully make spending decisions, work as fast as possible to deliver products/services

3. Challenges we ran into

[to be published]

4. Accomplishments that we're proud of

[to be published]

5. What we learned

[to be published]

6. What's next for White Wolf

[to be published]

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