Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Fort Wayne

AI is moving from pilot projects to frontline care in Indiana, and Fort Wayne already shows why: Parkview's Mirro Center secured a $175,000 NSF grant to build trauma‑informed design guidelines for mental‑health chatbots, exposing both promise and risk when automated tools miss subtle crisis cues (Parkview NSF mental health chatbot study).

Across the region, telehealth training - like the national 2025 Healthcare AI Bootcamp telehealth training - and workforce pressures documented in staffing reports make practical AI skills essential for clinicians and administrators.

For Fort Wayne providers and students looking to apply AI safely at scale, structured training such as Nucamp's Nucamp AI Essentials for Work bootcamp pairs prompt‑writing and use‑case practice with real workplace scenarios, turning local research and telehealth momentum into usable tools for better, safer care.

Bootcamp	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15-week bootcamp)

“Chatbots are trained on certain datasets. It is almost impossible to know what specific datasets were used... a majority have keywords and trigger words to get cues. But focusing on keywords alone may not provide the actual context. If a chatbot cannot recognize subtle cues and provide inappropriate responses or support, it's not just failing to meet expectations, it's creating an environment that is risky, unsupportive and dismissive of the individual's unique experiences and background, something evident in trauma-informed principles.”

Selection prioritized three practical filters to keep the list actionable for Indiana providers: (1) implementation evidence - use cases had to show real feasibility or published implementation data, such as the high feasibility reported for manualized interventions in clinical settings (implementation feasibility evidence in Implementation Science (2024)); (2) direct operational impact for Fort Wayne systems - examples include capacity and staffing tools that optimize OR schedules and clinician allocation (Nucamp AI Essentials for Work - predictive capacity planning registration and program details); and (3) workforce and governance readiness, measured by local training, policy and ethics activity documented at regional events (Fort Wayne Teaching and Learning Conference AI sessions and regional training).

Each candidate use case required at least one supporting source from these categories and a clear “so what” for Fort Wayne operations - e.g., potential to cut documentation time or smooth staffing peaks - so the final ten focus on near‑term, evidence‑backed deployments with local training paths available.

Selection Criterion	Representative Source
Implementation evidence	Implementation Science proceedings (2024) - implementation feasibility evidence
Operational impact (staffing/OR)	Nucamp AI Essentials for Work - predictive capacity planning program and registration
Workforce & governance readiness	Fort Wayne Teaching & Learning Conference - AI sessions and workforce readiness

Radiology at Parkview Heart Institute anchors Fort Wayne's cardiac imaging capacity with MRI, CT, PET fusion and same‑day x‑ray services - and those readily available scans create a practical entry point for AI‑powered assistance; a recent deep‑learning chest X‑ray model trained on roughly 25,000 X‑rays and echocardiograms outperformed board‑certified radiologists in detecting structural changes linked to heart failure, suggesting routine imaging could flag patients earlier for cardiology follow‑up and reduce diagnostic delay (Parkview Diagnostic Imaging services and same-day appointments, AI chest X‑ray deep-learning study detecting heart failure).

Parkview's on‑campus Heart Institute setup - hybrid suites, PET fusion on the Regional Medical Center campus and multiple regional locations - means any validated AI triage layer could translate into faster referrals and more targeted use of advanced imaging in northeast Indiana (Parkview Heart Institute Fort Wayne location).

Parkview Heart Institute - Address	Phone
11108 Parkview Circle Entrance 10, Fort Wayne, IN 46845	(260) 266-2000

“To date, the best AI model was 90% as accurate as a human expert... Now we are saying, if you think about the paradigm in a different way, you can actually exceed human performance.”

Fort Wayne systems such as Lutheran Health Network can cut unplanned readmissions by operationalizing near‑real‑time predictive scores into daily workflows: Healthcare teams that followed this path pushed an EHR‑integrated probability score to clinicians at the point of care, reviewed risk during morning huddles, and coordinated case management follow‑up - Children's Hospital of Orange County reported average seven‑day readmissions falling from roughly 4% to about 3% after doing exactly that and improved model AUC from 0.79 to 0.822 using cloud toolsets (HIMSS Readmission Rate Risk Predictor case study).

Peer work shows similar gains are attainable with local data: Mission Health developed a machine‑learning predictor that outperformed LACE (AUC ≈ 0.784) and made scores available early enough to change discharge planning (Mission Health machine‑learning readmission success story).

Practical steps for Lutheran include assembling a multidisciplinary team, integrating scores into EHR dashboards, and standardizing interventions (PCP scheduling, telehealth checks, SDOH screening) so the three daily high‑risk flags translate into one fewer avoidable readmission - training and change management can follow local upskilling programs (predictive capacity planning and training for Fort Wayne healthcare).

Metric	Value	Source
Seven‑day readmission rate (CHOC)	~3.8% (2013) → 3.3% (2019); average ~4% → ~3%	HIMSS Readmission Rate Risk Predictor case study
CHOC model AUC	0.79 → 0.822 (cloud)	HIMSS Readmission Rate Risk Predictor case study
Mission Health model AUC	0.784 (outperformed LACE)	Health Catalyst Mission Health readmission success story

Inferscience's HCC Assistant automates HCC capture and RAF optimization by ingested clinical notes, claims and EHR fields in real time, surfacing overlooked diagnoses and recommending codes at the point of care so Fort Wayne practices can close documentation gaps before claims are filed; the tool integrates with major EHRs, runs chart‑level gap analyses, and is SOC2 Type II–certified for data protection.

Pilot and case‑study data show automation can materially lift recapture - Inferscience notes that even modest RAF gains scale (≈$1M per 0.1 RAF improvement per 10,000 lives) - and product metrics report a false‑positive rate <3% with false negatives <1%, which limits noise for coders and clinicians.

For Indiana clinics seeking practical next steps, the Inferscience HCC Assistant product overview explains EHR workflows and implementation, while the Inferscience HCC recapture case study and RAF impact report details measurable RAF gains and workflow changes needed to realize them (Inferscience HCC Assistant product overview, Inferscience HCC recapture case study and RAF impact report).

Metric	Value
False positive rate	< 3%
False negative rate	< 1%
HCC codes presented (example rollout)	269K in 8 months (user report)
RAF impact benchmark	≈ $1M per 0.1 RAF per 10,000 lives

“Inferscience really speeds up the HCC process. It's a great tool for providers as well as coding personnel in catching opportunities throughout the entire chart.”

Franciscan Health's rollout of a patient chatbot - implemented with Enterprise Bot - demonstrates a practical Fort Wayne path for virtual health assistants: the case study reports a fully functioning bot standing up in under four weeks and achieving 88%+ accuracy at recognizing more than 100 FAQs, while Franciscan's broader virtual offerings (Franciscan Virtual Visit urgent care) show where chatbot triage and scheduling fit into live services; paired with evidence that chatbots provide 24/7 symptom assessment, appointment scheduling and medication reminders, this combination can cut staff time spent on routine calls and speed urgent‑care routing when properly governed and overseen (Franciscan Health Enterprise Bot case study, Franciscan Virtual Visit urgent care services, CADTH systematic review on chatbots in health care).

For Fort Wayne clinics, the clear operational takeaway is: rapid, low‑cost deployment plus human oversight can deliver 24/7 access without proportionally increasing staffing burden, provided privacy and escalation pathways are enforced.

Metric	Value / Source
Time to deploy	Under 4 weeks - Enterprise Bot case study
Recognition accuracy	88%+ accuracy; recognized 100+ FAQs - Enterprise Bot case study
Primary functions	24/7 symptom assessment, scheduling, reminders - CADTH review; integrates with virtual urgent care - Franciscan

Purdue's telehealth and wearable‑sensors research offers a practical blueprint for Fort Wayne telemedicine augmentation: the I‑EAT Lab is developing a reusable, low‑cost submental electromyographic sensor patch that conforms under the chin to remotely track muscle activity and laryngeal movement during swallowing - preliminary validation showed effectiveness in a Parkinson's patient and a healthy control - demonstrating how remote diagnostics can replace or triage in‑person specialty visits where swallowing experts aren't available (Purdue I‑EAT Lab telehealth and wearable sensors studies).

Paired with widely accepted remote‑monitoring use cases - wearables that transmit heart rate, glucose and other vitals to care teams - these tools make continuous, asynchronous follow‑up and rapid specialist consultation feasible for rural Indiana patients, reducing travel and speeding intervention when thresholds are crossed (Mayo Clinic telehealth overview and remote monitoring, HHS guide to telehealth technology for diabetes care).

For Fort Wayne providers, the tangible payoff is clearer triage, fewer unnecessary transfers, and the ability to run longitudinal swallowing or chronic‑disease monitoring programs from patients' homes.

Study	Focus	Funding / Status	Key finding
Purdue I‑EAT Lab wearable sensors	Submental EMG patch for remote swallowing evaluation	PIIN & Showalter Trust funded; active recruitment	Patch noninvasively monitored muscle and laryngeal movement; feasible in Parkinson's and control cases

Wearables that stream weight, heart rate and activity can form the backbone of a University of Saint Francis CHF monitoring program in Fort Wayne, feeding continuous vitals into the EHR and simple predictive models that flag early decompensation so clinicians can intervene before crises; pairing device streams with local implementation playbooks - such as Nucamp's AI Essentials for Work syllabus on predictive capacity planning (AI Essentials for Work: predictive capacity planning guide) - lets care teams prioritize outreach instead of reacting to admissions.

Automating routine charting with EHR documentation assistants reduces clinician time on notes so nurses focus on actionable alerts, while validated diagnostic tool workflows described in Nucamp's AI Essentials for Work syllabus on machine vision and diagnostic tools (AI Essentials for Work: machine vision and diagnostic tools guide) give remote assessments more objective context.

The concrete payoff: targeted telehealth check‑ins, fewer unnecessary ED visits, and staffing that scales proactively with patient risk instead of overtime.

AI-driven drug discovery and clinical‑trial optimization can let Fort Wayne researchers and health systems turn local data and modest lab capacity into faster, lower‑risk candidates by combining ML‑based ADMET modeling, generative chemistry and smarter trial‑matching: Simulations Plus' ADMET Predictor® packages AI‑driven ADMET modeling and AIDD modules to evaluate metabolism and toxicity early (Simulations Plus ADMET Predictor ADMET modeling software), broad reviews show ML improves virtual screening, target identification and lead optimization across the pipeline (PMC review: AI applications in drug discovery and lead optimization), and market analyses report dramatic gains - generative engines produced ~40 molecules within months and AI approaches can cut early timelines to 1–5 years while reducing R&D costs by up to ~40% - so what this means for Indiana: local academic labs, clinical trial sites and trainees can realistically participate in earlier, cheaper candidate assessment and patient‑matching, turning Fort Wayne into a practical node for preclinical candidate triage and streamlined site selection rather than a passive consumer of outside assets (DrugPatentWatch 2025 market analysis of AI-driven drug discovery).

Metric	Value	Source
Generative molecules synthesized	~40 molecules in months	DrugPatentWatch 2025 AI drug discovery market analysis
Projected R&D cost reduction	Up to ~40%	DrugPatentWatch analysis of R&D cost reductions from AI
ADMET/early safety modeling	AI‑driven modules (AIDD, ADMET Modeler)	Simulations Plus ADMET Predictor AI ADMET modeling

Fort Wayne clinics can convert the revenue‑cycle headache of manual claims into a predictable, lower‑cost operation by embedding claim scrubbing, real‑time eligibility checks and automated charge capture directly inside the EHR so claims leave the building cleaner and faster; industry playbooks show standardized, front‑end validation and centralized workflows cut denials and underpayments while making appeals and reconciliation far less manual (claims workflow best practices for healthcare revenue cycle - MDClarity).

Practical EHR‑level automation - auto‑coding suggestions, ERA auto‑posting and payer‑rule enforcement - reduces duplicate entry, enforces compliance, and accelerates reimbursements, a pattern Blaze documents when automated billing is built into the record rather than bolted on (automated billing inside the EHR best practices - Blaze).

The payoff is tangible: many providers lose roughly 5% of revenue to denials (~$5M for a typical hospital) and clinicians spend an extra ~15.5 hours weekly on paperwork, yet AI‑assisted scrubbers and denial triage can flag errors with >90% accuracy to reclaim both cash and clinician time (claims automation benefits and denial reduction - AppsRhino, automation accuracy examples for healthcare billing - Allzone).

Metric	Value	Source
Physician paperwork time	~15.5 hours/week	physician administrative time data - GetMagical
Denial revenue leakage	~5% of revenue (~$5M)	denial cost and revenue leakage analysis - AppsRhino
Claims scrubber accuracy	>90% flagging accuracy	claims scrubber accuracy examples - Allzone

“We're not replacing people; we're getting the mundane out of their day.”

Parkview's Precision Health program brings precision genomics into everyday oncology care in Fort Wayne by sequencing tumors and inherited DNA to match patients with targeted therapies, adjust screening schedules, and inform cardiology or neuroscience care when genetic risks overlap; the Mirro Center's Precision Genomics team (founded 2022) supports clinicians with interpretation and education so genomic reports translate into concrete treatment choices and earlier screening pathways (Parkview Precision Health overview, How precision genomics is used in cancer care).

Parkview's January 2025 entry into the Helix Research Network - and the DNA Insights community program that aims to enroll 100,000 people at no cost - means Fort Wayne patients can receive actionable risk results (about 1–2% test positive for screened conditions) and providers can use population data to tailor prevention and trial recruitment locally, turning genomic signals into earlier, more precise interventions (Parkview joins Helix Research Network, DNA Insights enrollment & benefits).

Metric	Value
Precision Genomics program founded	2022
Parkview joins Helix Research Network	Jan 15, 2025
DNA Insights enrollment goal	100,000 participants (region)
Expected positive rate	~1–2% for screened conditions
Time to genetic results	8–12 weeks

“Participating in the Helix Research Network will provide both individual and communitywide benefits. Patients will receive useful insights into their genes that can help prevent serious or chronic health problems, while the large pool of data will help Parkview better identify and understand the risk factors our population faces and allow us to tailor new programs or target services to address those issues.”

Fort Wayne providers should insist that vendors deliver explainable, auditable AI packaged with clinical‑validation evidence, clear regulatory mapping, and built‑in bias testing tied to local demographics - demands grounded in recent reviews of ethical and regulatory AI risks and clinical adoption barriers (PMC article on ethical and regulatory challenges of AI in healthcare, National Academy of Medicine perspective on barriers to clinical AI adoption).

A practical Fort Wayne vendor checklist includes model cards and data provenance, evidence of clinical utility (FDA/Cures Act alignment where applicable), SMART‑on‑FHIR interoperability, operator training and governance plans, and a commitment to publish post‑market performance dashboards with overall and subgroup metrics (AUC, false positives/negatives) plus documented mitigation for identified biases - one concrete test: require vendors to show stratified performance by race/ethnicity and ZIP code so hospitals can see whether an algorithm works for local neighborhoods before procurement and credentialing.

Adoption Domain	Vendor Deliverable
Reason to use	Clinical validation & reimbursement/evidence brief
Means to use	Interoperability spec (SMART on FHIR) & infra requirements
Method to use	Workflow integration plan, training curriculum, UI/alert design
Desire to use	Transparency, bias audits, liability allocation, post‑market monitoring

Fort Wayne providers and students should move from curiosity to concrete pilots: convene a small multidisciplinary team, pick one high‑impact use case (for example, an EHR‑integrated readmission score that teams review in morning huddles), and run a short, adaptive pilot using implementation‑science methods that document pre‑launch and early‑implementation modifications (implementation science article on adapting evidence-based interventions during initial rollout); practical evidence shows EHR‑driven readmission workflows can move seven‑day readmission rates from roughly 4% to about 3% when paired with standardized follow‑up (HIMSS readmission risk predictor case study).

Pair pilots with targeted upskilling - enroll care managers, coders and clinical leads in a focused program like Nucamp AI Essentials for Work - 15‑week bootcamp so prompt‑writing and workflow integration skills land quickly - and require vendors to deliver stratified performance metrics and SMART‑on‑FHIR integration.

The measurable “so what”: a short, documented pilot plus staff training can turn one tested AI score into fewer avoidable readmissions and a repeatable local playbook for other Fort Wayne use cases.

Program	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work - 15‑week bootcamp