Patient Pathways Through a Pandemic
This MLHub package uses the patientpaths package for modelling patient pathways through a health care system, particularly during a pandemic.
The patientpaths package is available from https://github.com/anu-act-health-covid19-support/patientpaths.
This MLHub package source code is available from https://github.com/gjwgit/patientpaths.
Quick Start
$ ml demo patientpathsUsage
-
To install mlhub (Ubuntu):
$ pip3 install mlhub $ ml configure -
To install, configure, and run the demo:
$ ml install patientpaths $ ml configure patientpaths $ ml readme patientpaths $ ml commands patientpaths $ ml demo patientpaths -
Command line tools:
TBD
Command Line Tools
TBD
Demonstration
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Patient Pathways
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Runs a model of care algorithm to identify outcomes from a configured
health care system. The input to the model consists of N cohorts
(e.g., age groups, gender, socio-economic, etc.). What the cohort is
does not really matter.
For each cohort the daily presentations of patients in that cohort
(i.e., the number of patients arriving each day to the health
facility) is provided as input. These are split into mild and severe
cases.
For this demo a spreadsheet of daily presentations is loaded. The
spreadsheet has two workbooks (tabs), one for the mild presentations
and another for the severe presentations. Each column corresponds to a
cohort and each row is a successive day. No headers are used in the
spreadsheet.
The other set of inputs (currently hard-coded) are the proportion of
the population in the ACT jurisdiction (2%), the number of beds in ICU
(22), the number of beds in wards (448), the number of beds in the
emergency department (202), and the total number of GPs (2,607).
Given 4 cohorts and daily presentations for 36 days we have, over the
mild/severe cases, 288 inputs.
Each cohort is also identified as at risk or not. For this example
cohorts 2 and 3 are considered at risk (the risk vector is 0,2,2,0,
and interpreted as Boolean ">1" though probably better to actually use
Booleans in the code).
Press Enter to continue:
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Model of Care
=============
The model of care is run to calculate the outcomes. The outcomes are
reported for: deaths, excess_icu, excess_ward, excess_ed_sev,
excess_ed_mld, excess_clinic_mld, excess_clinic_sev, excess_gp,
admit_icu, admit_ward, admit_ed_sev, admit_ed_mld, admit_clinic_sev,
admit_clinic_mld, admit_gp, avail_ed, avail_clinic, avail_gp,
avail_icu, and avail_ward.
The first set of outcomes reports the expected DEATHS per day per
cohort.
1 2 3 4 TOTAL
-----------------------------------
0.2 0.9 0.9 0.0 2.0
0.2 1.5 1.6 0.2 3.6
0.2 1.3 1.5 0.2 3.2
0.3 1.8 1.3 0.1 3.5
0.3 1.7 0.8 0.2 3.1
0.2 1.2 0.8 0.3 2.5
0.1 1.3 0.6 0.2 2.2
0.2 1.4 1.0 0.3 2.9
0.4 1.3 1.5 0.4 3.7
0.4 1.4 0.7 0.4 3.0
0.3 2.0 1.0 0.3 3.6
0.2 1.8 1.8 0.4 4.2
0.2 1.6 1.6 0.3 3.7
0.3 2.2 1.0 0.4 4.0
0.2 2.4 1.3 0.5 4.5
0.2 1.9 1.2 0.3 3.6
0.4 0.9 1.0 0.2 2.5
0.3 0.9 1.1 0.3 2.6
0.1 1.4 1.2 0.3 3.0
0.4 1.6 2.0 0.5 4.4
0.6 1.8 1.3 0.7 4.4
0.4 1.9 1.0 0.4 3.6
0.5 2.0 1.1 0.4 4.0
0.4 1.9 1.5 0.4 4.2
0.3 1.6 1.8 0.2 3.9
0.2 1.1 1.4 0.3 3.1
0.3 1.0 1.2 0.3 2.8
0.5 1.1 1.4 0.2 3.2
0.6 1.8 1.9 0.2 4.4
0.6 2.1 1.9 0.7 5.3
0.8 1.8 1.8 0.4 4.8
0.9 1.4 2.1 0.4 4.8
1.0 1.4 2.2 1.0 5.7
1.0 2.1 2.4 0.7 6.2
0.5 1.9 1.7 0.4 4.4
0.3 1.0 0.9 0.7 2.9
-----------------------------------
Press Enter to continue:
=====================
Saving to Spreadsheet
=====================
The results can be saved to a spreadsheet 'results.xlsx' with a
workbook (tab) for each of the measures listed above. From this
spreadsheet it is straightforward for spreadsheet jockeys to create
any required plots.
Do you want to save the results [y/N]? 
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Generating Plots
================
Pltos can be created from the input and output datasets. As a simple
example we plot the Expected Daily Deaths
Type Ctrl-W to close the plot.
Press Enter to continue: 