Organizing chronic care differently using digital tools. We focus on CLL (Chronic Lymphocytic Leukemia) in the Netherlands. This project was part of the deliverables of one of the courses in my master program.

1. Impact of AI diagnostics
Provide insight about care
Help optimize performance
Improve care experiences
Impact of automated blood
collection
Creates an oportunity for the
medical team to focus time on
more complex tasks
Organizing Chronic Care Differently - Case of CLL Care
CHALLENGES WITH THE CURRENT WAY INTEGRATED CARE IS PROVIDED
High costs & quality issues
Focused on cure
START PILOT WITH A 'RULES-​
BASED' CHRONIC DISEASE:
CHRONIC LYMPHOCYTIC LEUKEMIA (CLL) IN NL
THE WAY IN WHICH HOSPITAL-​
BASED CARE WILL BE DISRUPTED
Most common leukemia in adults ≥ 60 y/o, 14.000 patients in NL
Survival rate after 10y is 80%
More likely to affect men than women
Stage A, B (not active), B (active) & C
Usually asymptomatic
Flows should be organised by stage, notably:
Burnout staff
No prevention in CLL care leads to late
interventions, furthermore, lack of patient
support leads to lack of treatment plan
adherence.
Co-​
morbid patients
CLL care is organised around a specialist
hospital asking from the patient multiple
(unnecessary) visits (lack of processes &
support tools to monitor and treat at home).
Diagnosis, treatment plan development &
modification is still done by specialists which
leads to longer waiting times & more
expensive care.
Introducing prevention through bio-​
marker blood research & well supported
patients through a hub organised Benelux
patient network & elaborate patient aids.
With AI diagnostic & AI treatment plan tools,
care is further transferred to nurse
practitioners in a separate entity: a specialised
CLL hub, to build on volume & specialisation.
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'RULES-​
BASED' = EMPIRICAL / PRECISION MEDICINE
Stage A + Bna
Monitoring
Stage Ba + C
Treatment
X
Diagnosis & treatment of this type of care needs
disruption and can be handed-​
off to nurses
(Christensen et al., 2009).
CLL is likely typed as medium consequences &
little to average behavioural changes which needs
an integrated care network business model.
CLL
​
STAFF
​
CHALLENGES*
Patient = less mobile & frail -->
Complicated for hospital IV treatments & blood checks
Expensive hospital delivered medication
Patient has to travel to the hospital to collect medication
Complicated shared decision making
Treatment, duration, side-​
effects > patient needs more support
PROVIDER
CHALLENGES
* Interview with oncology nurse &
specialist highlight 3 challenges
INTUITIVE
CHRONIC
DISEASES
RULES-​
B
ASED
CHRONIC
DISEASES
CLL
reasoning
box
PROPOSED DESIGN CHANGES IN THE CLL PROCESS
Impact of a physical support
robot in the patient's home
Offers medical support and
company to the patient
FROM
TO
X
More patients are treated at home using robots
to support blood monitoring & home treatment
(e.g. to track respect of treatment plan), supported
by a tele-​
monitoring service at the hub.
Animated video STS project
Vimeo
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2. Process
SOLVING ETHICAL CONSIDERATIONS
Would you trust a robot to
draw your blood?
Who owns the data?
Who accesses the data?
Who is responsible for
the robot's actions?
KEY STAKEHOLDERS
Following the principle of
right treatment at the
right care delivery level ...
... A CLL Management Hub
will take-​
over a host of what
is currently standardised
hospital care
IN SUMMARY, HOW THIS PILOT IS A SOLUTION TO THE CHALLENGES
Few Hospital visits &
care at right level
Reduced medical
errors (e.g. < 1%)
Targeted treatment &
home-​
treatment
Efficient use of staff's
time
Prevention &
early diagnosis
from
to
FROM CURRENT FLOW ..... TO FUTURE
ON WHAT ARE THESE CHANGES BASED?
> patient involvement
< hospital activities
creation of a hub
Treatment takes place in the hospital;
large role for specialists, too much?
Patient is a subject,
moving many times
reasoning
box
reasoning
box
Following EU General Data
Protection Regulation
Collaboration with
Rutgers University
Blood drawing & analyzing
robot with 97% success rate
Partnering with Irdeto
Cybersecurity for healthcare
Collaboration with
Shanghai Association of
Science & technology
AI Diagnosis & staging
with 89% accuracy, 86%
sensitivity & 95% specificity
Collaboration with KPN
Seamless sharing of
healthcare data, lab results
& charts
Data protocols ensure privacy
-->insurance discounts
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2
Horizontal
Vertical
3
4
Electronic Patient Files
Elecronic request for blood test (hub)
Referal to Hub or Hospital
GP = Gatekeeper
GP Hospital
Home
improvement AI:
Work meetings: supervising team + technical team
feedback from nurses
problem solving meetings between nurses
Hub
AI does the diagnosis
treatment is validated by specialist
Robot provides treatment plan to
patient
Robot collects data
data is first verified with
hub, before insurance
companies
Vertical
Vertical
Horizontal
Horizontal
Hub
Hospital Home
High costs & waste Burnout staff
Co-​
morbidities &
treatment of symptoms
DIGITAL IMPACT ON PROFESSIONALS
Creates a new digital technology-​
educated workforce
Ability to work remotely from the hub
​ ​ ​
Professionals empowered to focus on the challenging cases
STREAMLINED COORDINATION MECHANISMS
Typical 5-10% diagn. /
treatm. errors
Expensive hospital
delivered medication