Results from engagements I led.
Client identities are withheld to protect confidentiality. Figures are drawn from engagement records, and I’m glad to walk through how each is measured under NDA. These engagements are in healthcare, where my record is deepest; the approach carries across regulated industries.
Turning around a complex healthcare IT operation
The challenge
- No formal SLAs, missing metrics, and inconsistently used tools
- Delivery that depended on tribal knowledge and a few key individuals
- A 1,274-item aged backlog and 38 production outages in the program’s first quarter, a sign of constant firefighting
- A mandate to stabilize business-critical production while building a repeatable delivery model
What I did
- Stabilize: stood up a 24x7 NOC, L1, L2, L3 triage, and production deployment guardrails
- Standardize: put ITSM and ITIL and a software delivery lifecycle in place, with SOPs, a service catalog, and baselined SLAs and KPIs; trained 100% of staff on ITSM
- Automate: analyzed 16,968 incident tickets, found about 30% automatable, and deployed alert correlation, routing, and job monitoring
- Modernize: moved delivery to a modern work-tracking system with Agile practice, and built the disaster-recovery and secure-development runway
The outcomes
| Area | Before | After |
|---|---|---|
| Quarterly production outages (first versus final quarter of the transformation) | 38 | 3 (92.1% fewer) |
| Aged backlog | 1,274 items | 0 |
| Critical incident recovery time | 88 minutes | 17 minutes (5x faster) |
| Ticket closure rate | 94% | 98% |
| First-time resolution | not reported | 98.53% |
| System availability | not reported | 99.98% |
| NOC tickets created proactively | manual triage | 99.2% |
| Manual effort avoided through automation (estimated) | Manual processes | 3,500+ hours per month |
| Client satisfaction | 68% | 87%, in 90 days |
| SLA compliance | no SLAs | 99.5% against baselined SLAs |
The 3,500+ hours figure is an estimate of recurring manual work avoided by automation, not a headcount reduction.
Rationalizing a 350-application portfolio into a funded, sequenced roadmap
The challenge
Leadership needed one fact base for deciding where to invest, where to consolidate, and what to retire across a large portfolio that had grown over many years.
The approach, at a high level
- See it. Inventory the portfolio and map each application to the business capability it supports.
- Judge it. Assess business value and technical fit on one consistent scale, so the conversation moves from opinion to evidence.
- Cost it. Build a total-cost-of-ownership view for each application.
- Decide it. Give every application a disposition from a standard set of six options, then sequence the work into waves leadership can fund one at a time.
What I delivered
- An application-to-capability map
- A business and technical fit assessment
- A total-cost baseline
- A disposition for every application
- A three-wave, multi-year investment roadmap
- An executive readout that secured sponsor buy-in for Wave 1
The financial view
| Item | Result |
|---|---|
| Total-cost baseline | More than $28 million across the portfolio, built application by application |
| Investment structure | Three waves over about three years, sequenced so leadership can fund one at a time |
| Decision secured | Executive sponsor buy-in for Wave 1 |
One shared enterprise platform that business users largely run themselves
The challenge
- Serve two audiences from one platform: solutions exposed to member organizations across the country, and enterprise solutions used by internal business units
- Keep demand disciplined by separating real business needs from wants
- Avoid duplicate solutions, duplicate licenses, and a different security model for every project
- Automate complex, multi-step business workflows that touch regulated data
- Build adoption with no positional authority over the teams that had to adopt it
What I did
- Needs before wants: every request was tested against the business outcome it had to deliver, and the solution was sized to the need
- Reuse first: shared components, data models, and patterns before any new build
- Secure access by design: one governed access model for external and internal users, so a new solution inherited the controls instead of reinventing them
- Shared licenses: pooled licensing across solutions in place of project-by-project purchases
- Low-code and no-code first: configuration over custom code, so routine change belonged to the business rather than a development backlog
- Workflow automation: complex approval, case, and service workflows automated on the platform
The platform footprint
Service Cloud, Community Cloud, Sales Cloud, Marketing Cloud, the Lightning Platform, and CRM, used for national-scale service and engagement solutions as well as internal enterprise solutions.
The outcomes
| Area | Before | After |
|---|---|---|
| Solutions on the shared platform | 1 | 10, over three years |
| Production changes handled by business users | not reported | About 80% of the changes needed |
| Licensing and security model | per project | Shared and governed |
Migration readiness and cutover risk reduction on a core healthcare platform
The challenge
- Move core transaction processing to the new platform across a multi-vendor ecosystem
- A spring pilot go-live ahead of a larger mid-year go-live
- A timeline compressed by roughly two months after contract finalization paused work already under way
- Real risk to live member and customer transactions
What I did
- Framed the decision for leadership as two risk-mitigated go-live options instead of one high-risk cutover: phased enablement with parallel, dark-mode testing, or a controlled run-out on the legacy system with a clean cutover
- Defined date-of-service cutover logic so business teams could validate outcomes before live processing
- Owned the vendor testing process and change control across every partner
- Sequenced the core interfaces to protect member operations
What I delivered
- A clear, defensible go-live recommendation that minimized disruption and production risk
- Date-of-service and run-out logic the business could test before it went live
- A coordinated multi-vendor test-and-cutover plan under an externally compressed schedule
Delivering an EMR and practice management suite for behavioral healthcare
The challenge
- Give behavioral healthcare practices one system for the clinical record and the business side of the practice
- Fit the way clinicians actually work: caseloads, notes, treatment plans, and prescribing
- Connect to outside clinical systems and payer transactions using healthcare standards
- Meet HIPAA requirements for sensitive behavioral health records
What I did
- Led the software development organization that delivered the suite
- Owned the product engineering roadmap and release delivery, reporting directly to the CEO
- Worked hands-on with HL7 and X12 EDI interfaces to third-party clinical and payer systems
Key domains in the suite
Intake
Patient registration and referral intake.
Credentialing
Provider credentialing and onboarding.
Scheduling
Appointments, group sessions, and resource calendars.
Clinical transactions
The encounters and transactions that move through the clinical workflow.
Billing and claims processing
Billing, claims processing, and revenue-cycle support tied to the clinical workflow.
Provider search
Finding and matching providers for patients and referrals.
Clinical documentation
Clinical and progress notes, treatment planning, and customizable forms.
Pharmacy integration
Connecting prescribing to pharmacies.
HL7 messaging
Standards-based interfaces to outside clinical systems.
HIPAA compliance
Privacy, security, and audit controls for sensitive health records.
Available on request
Data platform modernization
Built a governed analytics foundation on Microsoft Fabric and Power BI for a healthcare client, and gave leadership a role-by-role fixed-bid versus time-and-materials decision.
Ask about this →Secure SDLC and cloud foundations
Led a delivery-maturity baseline, a secure CI/CD toolchain, and an AWS landing zone with security guardrails for a national healthcare organization.
Ask about this →AI solutions: secure knowledge systems
Designed a private, secure retrieval system for enterprise knowledge, and led enablement of enterprise AI tools in a regulated healthcare environment.
Ask about this →See your own situation here?
Tell me what’s happening and I’ll tell you how I would start.