With the explosion of Gen AI, companies are struggling to figure out how to start and where to focus. A blueprint for how to start thinking about and organize for successful AI pilots

1. How to Develop an AI
strategy for Healthcare Ops
CDAO Fall 2023
Niren Sirohi, VP Enterprise Analytics, Dana Farber Cancer Institute

2. Generative AI Buzz!
Generative AI is projected to grow faster in healthcare than any other
industry with a compounded annual growth rate of 85% through 2027*
* AI Total Accessible Market research, expert interviews, Boston Consulting group analysis
Hands free charting and
auto generated visit
summaries using GPT-4
Generative AI tools in Epic to
auto draft responses to common
time intensive messages
Using generative AI to
ease the search for
patient information
Generative AI solution for
hospital operations to
support decision making
for patient flow,
scheduling etc
NYUTron: LLMs for predicting
in-hospital mortality,
comorbidity index, 30-day
readmission, LOS, insurance
denial showed improvements of
5 to 15% in performance
Exploring applications to
automate processes such as
documentation, claims
handling, pre-auth, appeals,
patient onboarding

3. Excitement and Skepticism
CDO/CDAO
Generative AI can increase productivity and cost
efficiency, but only 6% of health systems currently
have a strategy—Bain and Company, Aug’23

4. Guiding Pillars for an effective AI Strategy
IDENTIFY HIGHEST
IMPACT USE CASES
PLAN FOR WORKFORCE
TRANSFORMATION
CREATE GUARDRAILS
• What skills and talent are needed?
• Modify roles and responsibilities for
combined human-AI workflows
• Prepare for risks
• Monitor quality and security
• Prevent bias
Implementing this strategy successfully will require close collaboration and
leadership across multiple departments: IT, HR, Legal, and Operations
• Where could we see a competitive advantage?
• Where is the most potential near-term value (e.g.,
documentation burden, workflow efficiency)?
• Where is the greatest long-term value?
WAYS OF WORKING &
INFRASTRUCTURE
• Agile testing/piloting process
• Staffing model
• Operating model
• Data Architecture and Infrastructure

5. Automation
of existing
processes
Analytics
Content
creation and
editing
including
code
Virtual
assistant or
expert
• Use of robotic process automation (RPA) and natural language processing (NLP) to automate existing
administrative processes (can be used in conjunction with generative AI to also draft content)
• Includes claims documentation and handling, staff onboarding, billing and scheduling, invoice processing
• Use of machine learning (ML) and other advanced analytics techniques across descriptive, prescriptive,
and predictive models; often seen as “traditional AI”
• Includes optimization of operations (scheduling, supply chain), forecasting of patient volumes, adverse
events, re-admission, risk profiling (for patients, payors, and other stakeholders)
• Utilizing generative AI to develop content (images, text, video, code) or edit existing content (including
translating language)
• Includes drafting emails and other communication materials, generating code (and reviewing code),
create marketing materials, generating post-visit summaries and instructions as part of care
coordination
• Utilizing generative AI to extract insights from unstructured data (images, PDFs, clinical notes, voice
recordings) or across different data sources
• Includes intelligent form extractors for claims processing, real-time assistance for contact agents and
schedulers, patient self-service for translation and navigation, improved intranet search function for
HR/IT
AI capabilities for Operations IDENTIFY HIGHEST
IMPACT USE CASES

6. Impact Feasibility
Estimated
value
What is the estimated
savings from implementing
the use case (e.g., hours
saved, cost avoided)?
Secondary
benefits
Will this contribute to
improved staff
engagement and/or
retention?
Can this become a
potential new revenue
stream?
Proof of
Concept
Do we hit a positive return
on investment within the
next 5 years?
Is there a potential
partner or commercially
viable solution?
Are there other case
studies of hospital
systems implementing the
use case?
Is there a way to run a
smaller pilot before
implementing broadly?
Do we have the technical
capabilities (people,
infrastructure, and data)
to implement the
solution? Or if not, can we
develop this in less than
a year?
Technical
Capability
Interest
Ownership Does the use case have
a champion who can
lead the
implementation?
Is the effort required
to implement (in
terms of man hours,
training, data clean-
up) commensurate
to the estimated
value?
Is there a risk to
patient care?
Effort
Required
Will this contribute to
improved patient
experience?
Prioritizing use cases: Framework IDENTIFY HIGHEST
IMPACT USE CASES

7. 1 Chime Central | 2 CAQH | 3 KPMG | 4 Healthcare Financial Management Association Survey
| 5 McKinsey Revenue Cycle Success Cases
Case for change
8x amount of more work
done per minute than manually1
32-88% cost savings on
spend per transaction per
RCM process depending on
level of automation2
25 – 40% of revenue cycle
costs could be reduced using
RPA for hospitals3
2/3rds of health systems
use revenue cycle automation in
20214
Revenue cycle areas by adoption across health systems5
Front end • Out-of-state billing • Payment enablement
• Point of service collections
• Insurance verification
• Eligibility and enrollment
services
• Pre-authorization and pre-
certification
Midcycle • Revenue integrity: charge
description master
• Health information
management
• Revenue integrity: inpatient
code validation, charge
capture
• Coding
• Transcription
Back end • Financing • Contract management and
modeling
• Third party collections for
debt
• Clearinghouse
• Prebilling scrubbing
• Billing
• Payment posting
• Zero-balance underpayment
• Denials and appeals
management
• A/R Follow-up
• Coverage discovery
• Transfer diagnosis-related
group
• Generate bills/patient
statements
Few applications
High-interest,
experimental apps
Increasingly frequent
applications
Rev Cycle Management Use Cases IDENTIFY HIGHEST
IMPACT USE CASES

8. Be safe but don’t stifle creativity CREATE GUARDRAILS
• Deploy private versions of ChatGPT/other LLMs (Cohere, Anthropic etc)
• Educate staff on the risks and benefits but encourage use
• Crowdsource productivity improvement ideas and incentivize
participation (Current generative AI and other technologies have the
potential to automate work activities that absorb 60-70% of employee
time today. Source: McKinsey)
• Embed and leverage gen AI in existing S/W applications e.g.
Microsoft365 Copilot etc.

9. Governance Layer
Core Working Group
(Coordinating Layer)
• Small team of (3-4) dedicated resources
• Develops use-case definitions, valuation, prioritization framework
• Recommends pilot projects
• Business stakeholder engagement and coordination across functions
• Defines pilot team structure, composition, needs
• Provides alignment to pilot teams for purpose, strategy, priorities, and ways of working
• Identifies partnerships with external providers and contracts
• Coordinates and guides movement of pilots across AI project stages
• Education and training, scaling strategy and support
• Responsible for execution
• Cross functional with representation from business unit, analytics, tech, partners
• Team members have portion of their time dedicated to this work and can rotate over time and initiative
stage
Executive Committee:
COO, CFO, CHRO, CMO, CDAO
• Approves strategy, goals, resources, budget
• Convened by CDAO
• Ensures org-level prioritization & alignment
Steering Committee:
Senior representation from relevant
business areas, IT, HR, Legal, + Core
Working Group leader
• Sets strategy, prioritizes individual projects
• Reviews progress & problems presented by Core Working Group
• Ensures appropriate resource allocation
• Removes organizational boundaries, de-silos team
Project Teams
New processes and structures WAYS OF WORKING &
INFRASTRUCTURE

10. 10
ENVISION PROTOTYPE INCUBATE SCALE
Formulate high
impact use cases
•Collaborate closely
with operational
leadership to define
goals and use cases
•Have clear
mechanism(s) for
defining value
(patient benefit,
engagement,
financial ROI, etc)
Develop prototypes
for use cases in an
agile manner
•Use external partners
for agile prototyping
when available
•Assess feasibility
•Summarize context,
approach, and impact
•Assess sources of
value
For select use cases,
conduct agile pilots
•Validate impact
•What does MVP look
like?
•Implementation
challenges and
workarounds
•What staff, tools, and
infrastructure do we
need to pilot, and
then to scale?
Operationalize and
scale
•Ensure the right tools &
tech are being
leveraged
•All components built
with scalability in mind
•Reimagine processes
and embed AI
•Rethink human-AI
interactions
•Enable governance and
value measurement
•Adjust operating model
Agile: 6 months
WAYS OF WORKING &
INFRASTRUCTURE
AI project journey

11. 11
WAYS OF WORKING &
INFRASTRUCTURE
Renewed focus on Data
RAG (Retrieval Augmented
Generation) is the Rage!
• Well organized unstructured data stores with metadata
tagging
• Pre-process data to handle PII, file formats etc
• Vector databases and embeddings
• LLM integrations if needed using commercial tools like
Langchain
• Prompt engineering, testing, evaluation
• Data quality measures needed at each step
• Data engineering talent increases in relevance relative to
data scientists
Good data Needed along with a
robust Solution Architecture!