Decision models and evidence that hold up — for healthcare and life science leaders.
Health economic models, market access strategy, and commercial and financial forecasts for medical device, diagnostics, pharma, and hospital-system organizations — built to stand up to the people who scrutinize them: payers, boards, investors, and health-system leadership.
Six ways to engage. Each one ends with something you can use.
Every engagement is scoped around a specific decision and produces working deliverables — models your team can run, documents you can put in front of a payer, a board, or an investor.
Decision Readiness Review
For teams facing a payer meeting, fundraise, or launch decision who aren't sure their numbers will hold up.
A structured review of existing models, evidence, and assumptions against what the audience — payer, board, or investor — will actually test.
You walk away with
- Written gap assessment of your models and evidence
- Prioritized fix list mapped to your decision timeline
- A working session to walk through it with your team
Economic Value Model
For companies that need to show payers, hospitals, or health systems the financial case for adoption.
Budget impact, cost-of-care, and stakeholder economic models built from claims, literature, and real-world data — with every input sourced and every assumption testable.
You walk away with
- Working Excel model with scenario and sensitivity controls
- Payer- or hospital-facing value deck
- Technical documentation and source library
Market & Adoption Model
For teams that need a defensible answer to "how big is this, and how fast does it ramp?"
Epidemiology-based market sizing, patient-flow modeling, and adoption forecasting grounded in analog products, clinical pathways, and utilization data — not top-down percentages.
You walk away with
- Bottom-up market model with segment detail
- Adoption forecast with documented analogs
- Board- and investor-ready opportunity summary
Investor-Grade Financial Model
For founders and CFOs preparing to raise, price a round, or plan commercialization spend.
Integrated pro formas that connect clinical and market assumptions to revenue, operating cost, headcount, cash flow, and capital requirements — built to survive diligence.
You walk away with
- Driver-based financial model, fully editable
- Scenario cases: base, upside, downside
- Fundraising narrative tied to the numbers
Life Sciences Commercial Analytics
For pharma and biotech commercial, market access, and finance teams who need clarity on demand, access, and net revenue.
Drug utilization and demand forecasting, gross-to-net analysis, and market access analytics that connect prescribing, payer coverage, and pricing to the revenue that actually lands.
You walk away with
- Demand and utilization forecast by segment and channel
- Gross-to-net bridge with rebate and access scenarios
- Market access and coverage-impact analysis
Fractional Analytics & Health Economics Leadership
For organizations that need senior analytics, HEOR, or medical economics leadership without a full-time executive hire.
SI360's principal has built this function inside a healthcare company before — the team, the models, the evidence systems, and the executive reporting. Fractional engagements bring that same leadership to your organization: setting analytical strategy, building and reviewing models, standing up evidence infrastructure, and representing the numbers to boards, payers, and investors.
Typical scope
- Recurring monthly capacity with defined priorities
- Ownership of model development, evidence strategy, and the analytics roadmap
- Direct participation in payer, board, and investor conversations
The work, in detail.
Women's health medical device: from clinical promise to fundable plan
Development-stage medical device · Women's health · Engagement type: Market, Economic & Financial Models
The situation
A development-stage women's health device company had strong clinical signals but no quantified answer to the three questions every investor and partner asked: how big is the market, what are the economics for providers and payers, and how much capital does the path to market require?
What SI360 built
- Epidemiology-based market model with patient-flow and adoption forecasting
- Provider and payer economic models, including site-of-care and reimbursement analysis
- Health technology assessment framework for evidence planning
- Integrated investor-grade pro forma linking clinical milestones to capital needs
How it was used
The models became the analytical backbone of the company's fundraising and partnering conversations. Leadership used the market and adoption model to set commercialization strategy, the economic models to shape the evidence plan, and the pro forma to size and structure the raise.
AI-enabled evidence platform: a hybrid-RAG system built for HTA rigor
Proprietary SI360 infrastructure · Engagement type: Evidence Systems · Full technical section below
The situation
Evidence review for health economics has a structural problem: screening and pulling data from research papers is slow, and general-purpose AI tools are unusable for the work because they invent figures and can't show where a claim came from. Meanwhile HTA bodies including NICE now expect any AI-assisted evidence to be justified, transparent, and human-supervised — a standard most tools weren't designed to meet.
What SI360 built
- A production hybrid-RAG platform: drop research PDFs in a folder, run one pipeline, and query the corpus through cited synthesis
- Layout-aware ingestion that reads economic tables — where ICERs, QALYs, and cost data live — as structured data, not garbled text
- An evidence scorecard that grades every source on quality and relevance, and factors that grade into what surfaces first
- Cited answers where every claim links to paper, section, and page, with explicit source-tier labels
What it proved
In a structured benchmark against general-purpose AI approaches — direct LLM answers and large-context document uploads — across seven HTA-relevant queries, the platform came out ahead on the measures that matter for evidence work: how well answers were grounded in real sources, how clearly each was traced back, whether the system admitted what it didn't know, and how completely it covered each question.
Precision diagnostics: building the payer case before launch
Emerging diagnostics & healthcare technology · Engagement type: Economic Value & Market Access
The situation
Emerging diagnostic technologies needed credible payer and provider evidence ahead of commercialization — and the coverage conversation was coming whether the evidence was ready or not.
What SI360 built
- Claims and real-world data analyses of utilization and cost patterns
- Cost-of-care and budget impact models by payer segment
- Payer value narratives translating the models into coverage arguments
- Executive dashboards tracking the market and evidence landscape
How it was used
The work anchored payer engagement strategy and launch planning, and gave leadership a shared, quantified view of where coverage was winnable first and what evidence each conversation required.
An AI evidence engine built to the standard HTA now demands.
Global HTA and regulatory bodies — including NICE and Canada's Drug Agency — are setting clear boundaries for AI in evidence generation: methods must be transparent, traceable, and human-supervised.
The SI360 Evidence Platform was built from the ground up to solve the core weakness of general AI models — unreliability with numbers. It pairs table-aware reading with strict source tracking to speed up evidence work without cutting corners on rigor.
Where the line sits: the platform speeds up screening, extraction, and evidence grading. Methodological judgment stays entirely with expert human analysts — and it doesn't replace a full systematic review.
The hard problem with AI in evidence work is fabrication. In testing, when a question had no answer in the sources, general-purpose tools invented figures and made them look real. This platform refused — and flagged exactly what was grounded and what wasn't.
Table-aware ingestion
Reads complex health economic tables as structured data instead of scrambled text, so the numbers that matter stay intact — each tied back to its source.
Evidence scorecards
Grades every source on quality, sample size, and relevance, so higher-rigor studies surface first rather than treating all sources equally.
Auditable citations
Every claim links to its paper, section, and page, and is labeled by tier — grounded in the sources, or a derived estimate — so nothing is taken on faith.
An analytics firm since 2009, focused on healthcare's hardest decisions.
Michael Fried
Founder & Principal
The firm: Strategic Insights 360 was founded in 2009 as an analytics and growth advisory, serving major brands across consumer, media, retail, and financial services on the customer, commercial, and operational analytics behind their growth decisions. Over more than fifteen years its focus has evolved from commercial analytics into healthcare decision modeling, health economics, and AI-enabled evidence development — the disciplines behind coverage, commercialization, and capital decisions.
The principal: Michael Fried leads SI360 engagements. Most recently he served as Vice President of Commercial Intelligence and Data Analytics at Alva10, where he built the analytics, health economics, and medical economics function from the ground up. He holds an MBA and an MS in Health Informatics.
Why the combination matters: health economics tells you what a technology is worth to the system; commercial analytics tells you how adoption actually happens; financial modeling tells you what it takes to get there. Most firms do one. SI360's value is connecting all three inside a single, consistent set of models.
Start with a conversation about the decision you're facing.
Reach out with the decision you need to make — a payer meeting, a raise, a launch, an evidence question. You'll get an honest read on whether and how modeling can help, and what it would take.
LinkedIn · Chapel Hill, North Carolina
Serving organizations across the U.S.