The Flu, Transmitted Infection Vectors & Healthcare Labour

800px-Students_assisting_surgeryCould in-house Facility Epidemiology manage healthcare $compensation costs?
David Huer, Vancouver, Canada.



In Health Care as in all public safety and assurance occupations, it is vital to protect the trained caregiver from inadvertent harm because that person’s loss damages society’s ability to serve everyone.


I’ve been at home with flu for a week. Friends from the States came to visit throughout the Lower Mainland and up the coast, then we had a visit. Shortly thereafter, I got walloped. And then, whilst talking to them on the phone later, learned that a group of students, coughing and sniffly, came onto their connecting flight. And learned from my family physician that flu symptoms do not show for 12 hours after exposure. Making us suspect they might be the source vector.

Periodically, I volunteer business services to health clinics, and it was in thinking about that, and the vectoring of this flu, that I’ve been wondering:

Could we recognize a role for Transmitted Infection Vector (TIV) in $compensation rates at a healthcare facility?

Could we alter our definition of effectiveness in patient-centred care, to one where Health Worker salary $compensation is weighted to account for exposure to Patient Transmitted Infection (PTI). Recognizing the likelihood that the worker could become a Transmitted Infection Vector (TIV)?

  • Identifying different risks of exposure of different classes of health worker?
  • Recognizing that different classes have different risk to become TI Vector transmitters?
  • Could this become the means to improve $compensation package effectiveness?

TIV Proximity Risk



Dartmouth’s Chris Trimble neatly summarizes healthcare innovation pathways at the Dec 2015 article here:

  1. Standardize and delegate
  2. Coordinate
  3. Prevent
  4. Improve treatment decisions

All 4 paths influence this idea, and gathering the data set is enormously challenging if collected with traditional paper and interviews. But, local WiFi/GPS (Smartphone, Geo-fenced In-House Smartphones, Active ID cards) and machine-learning – accessed through programs like Samsung’s Enterprise Alliance Program – offer a nearly automated data-gathering method, that could make this an effective, scalable process and tool for private, public, and mixed-model social enterprise health facilities.


Building the Combined Model (Actuarial Model + Epidemiological Model) from source data


  1. Determine Facility Factors
  2. Determine Human Factors
  3. Determine Budget Factors.

Building the Actuarial Model from source data


  1. Build and test the Actuarial Model


  1. Gather “employee path-taking data” to develop epidemiological data for analysis of likely TIV source-paths and source-points.
  2. Develop Epidemiological Model


  1. Combine Actuarial and Epidemiological Models
  2. Negotiate $Compensation Packages


Using the University of New Mexico’s Dental Clinic and Surgery Center.

Rough calculations look interesting:
(see supplementary calculations below coloured hospital maps)

In a traditional payout, the RN Administrator might have highest pay, derived from:

  • Years of Service
  • Seniority
  • Assumed accumulated technical expertise
  • Assumed accumulated humane-contact expertise
  • Terms of a Collective Agreement

In this model, the RN Surgery Nurse is deemed to be likely to face higher risk of becoming a TI Vector. Therefore, to protect the continuing presence of this person as a public resource, s/he is:

  • Deemed to require higher compensation for the risk.
  • Able to choose career paths using the Risk/Compensation ratio [ R/C ].
  • Able to pre-select or accept shift assignments shifts using R/C Ratio guidelines.




The implications are interesting:

  • Health workers of all types (nurses, physicians, security guards, waste cleaning workers, nurse practitioners, administrative staff, mechanical engineers, etc.) experience different levels of stress – and each person’s ability to manage their exhaustion, to obtain compensation, can be hugely challenging. As are suicide and trauma experienced from patient violence and administrative overload. Could TIV-adjusted $compensation packages bring about a new harmoniousness of purpose among health facility staff?
  • Could WiFi/GPS/Machine-learning be used to calculate a fluctuating Premium that adjusts to the ability of RNs carrying out their duties?
  • Could a higher status Surgeon Emeritus (SE) get lower compensation relative to an Active Surgeon (AS) when the SE faces lower daily risk? 
  • Could $Premium compensation go up during a full moon or after a sports match? Will workers choose to pick that busy shift knowing the R/C compensation rate?
  • Would unexpected source-points and source paths be discovered? For example, the handling path for medical waste held as evidence by a police constable?







Students assisting surgery in an affiliated hospital of Hebei North University., Author: CMSRC, 1 April 2008, 21:27:07, Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0).

Could Cloudbox Mimics improve the naturalness of machine-learning?

Creating a “Cloudbox Mimic”
to map Rhizome growth choices, as a self-comprehended ‘hypotheses testing’ learning tool of ever-enlarging complexity

Would ‘asymmetric logic’ help machine-learners practice natural learning?

dhuer-cloudboxing-aIn 2014, I developed the Cloudboxing© thinking technique. Teaching myself to stitch together a set of cognitive cloud datapoints to create a place to study the building blocks of coding language, to learn exactly what code was and where it could be located in my data set. ie. Using my first cognitive language (Liquid Membraning) to translate coding language into the “building blocks” of Liquid Membraning language. See Project #5 at

Lately, in between work, consulting, and venturing, I’ve been thinking about machine learning and Google’s DeepMind project, and wondering whether the “flatness” of programmed teaching creates limits to the learning process? For example…whilst reading the Google team’s “Teaching Machines to Read and Comprehend” article

Could we enlarge the possibilities, using spatial constructs to teach multidimensional choice-making?

creating a cloud-box to mimic rhizome growth choicesThis could be a software construct, or a physical object [such as a transparent polymer block, where imaging cameras record choice-making at pre-determined XYZ coordinates to ensure the locations of choices are accurately mapped (especially helpful when there are multiple choices at one juncture)].

Encapsulating and organizing defined space for machine-learned self-comprehension. mimics the “cloudboxing” technique.

And, it mimics the natural self-programmed logic of self-learning…a novel teaching tool for the machine-learning entity:

  • Creating a set of challenges through 3dimensional terrain that mimics pre-defined/pre-mapped subterranean tunnels
  • Creating an opportunity to dimensionally map an emulated (or actual) entity growing through the tunnel system
  • Studying the polar coordinates of the entity traversing the pre-defined space(s)

1) What about using a rhizome?Jiaogulan-Rhizome

. . . Using a natural entity teaches a machine-learning entity to mimic natural learning.

Using a plant creates the possibility that we can map choice-making, using attractants such as H2O and minerals, as a mimic for conscious entities developing learned behaviour.


2) Once you have a defined baseline data set, could machines learn better if being blocked and shunted by an induced stutter?

using stuttering blocks to teach choice-decision-making

Perhaps learning by stuttering and non-stuttering might produce interesting data?

By creating a stuttering event as the baseline, perhaps the program will use this to overcome obstacles to the learning process as well as the object of the lesson to learn to not stutter? This could produce a host of interesting possibilities and implications.

3) Things get incredibly interesting if the program eventually attempts to produce choice options outside the available options . . .

Note: These ideas continue the conceptual work of WarriorHealth CombatCare, re-purposing the anti-stuttering Choral Speech device SpeechEasy for Combat PTSD treatment. The research proposal for that work is here:


Jiaogulan-Rhizome: Own work/Eigenes Foto by Jens Rusch, 29 August 2014  CC Attribution-Share Alike 3.0 Germany license.

Drawings: David Huer © 2014-2015

Will Angel List post Impact ROI Target scores?

I posted my profile at Angel List yesterday

It’s a neat site, but I noticed something funny. The job board breaks out hundreds of jobs the usual way, by location, job title, compensation…

But no Impact ROI Target Scores.

This may be restricted to US site visitors. But if not, I want more. When looking for contractors and employees, or a team to join, I want something that matters, to my definition of what “matters” for me. Angel List, the go-to site for startups offering job opportunities across the English-speaking world, could provide that data. Here’s what I am asking for: a Balanced Scorecard filter that lays out key impact targets determined by the founders, team and investors.

As a yard/meterstick, all measures are good.






If the target is to . . .

  • Maximize investor returns as a pure fintech money machine play…that’s great.
  • Create a gene-splicing tool for skeletal dysplasia…that’s also great.
  • Create incentives to stop the bear paws trade…again, that’s great, too.

Crunching Impact ROI targets into a neutral benchmark, and job-comparing tool. That’s dollarable value-add.

Will Angel List invite startups to post targets? 

The Door to $Capital Heaven


A country’s embassies and consulates are that nation’s territory, subject to its laws, rules, and customs.

Some time ago, I asked the US State Department to create an incubator inside the Vancouver consulate.

Every day, I could walk to my office in America without having to leave Canada. An embassy official appreciated the creativity but said no dice.

Hence the “Border Entrepreneur Visa” proposal, posted earlier this week:




Image: Seal of the US Consulate, Vancouver, BC. Public Domain: –

Replacing the US EB-1 and E-1 with a BE-1 Visa?

Many entrepreneurs need to follow the money to Silicon Valley. But US visa programs can be a challenge.

Blueseed Ventures offered a solution: Putting a tech incubator in international waters with a ferry traveling to San Francisco every day.

Lots of interest but not enough funders. Not to mention legal entanglements such as border controls and inspections, all rightfully a responsibility of the United States to its citizens.

Is a new visa program, paradoxically, the easier way?

100 miles inland & 100 miles offshore

Could the US create a new “Border Entrepreneur visa (BE-1)” that allows entrepreneurs to enter and operate startups and companies on US soil, to get access to US capital, without living in the USA?

Here is my thinking:

  • 100-mile border zone inside US land border
  • 100-mile border zone adjacent to the coastline of US states and territories
  • Visa region includes US territories (Puerto Rico, Guam, US Virgin Islands, etc)
  • US citizens get priority hiring as employees (core team exempt from the rule)
  • Mutual benefit for entrepreneurs and US economy
  • US evaluates entrepreneurs without having the entrepreneur burden US services

Washington State/BC Border Zone

Example: a BC entrepreneur could operate in Whatcom County (Blaine, Bellingham, Ferndale) and return home to Vancouver every evening. Or simply move to Seattle or Tacoma.



What would this do for Plattsburg, NY, or Detroit, MI, or Yuma, AZ?

Could this alleviate the ‘undocumented alien’ challenge along the Mexican border? And produce certainty for authorities and law-abiding immigrants?

Could BE-1 visa holders demonstrating good citizenship and contributions to the economy “graduate” to the resident visa/citizenship stream?

Could the BE-1 produce legal/economic certainty for sea-stead ventures located between the coastline, and the 200-mile Exclusive Economic Zone or 12-mile offshore treaty border?