Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Billings

In Billings, Montana, rapid infrastructure and workforce shifts make practical AI adoption a priority for regional care: the state is weighing a new eastern Montana mental hospital site in Billings as demand grows (KTVQ report on Billings eastern Montana state hospital site selection), Intermountain is constructing a technology‑ready St.

Vincent replacement with 243 planned beds and operating rooms designed for robotic surgery and ICU flexibility (Intermountain St. Vincent replacement hospital construction and robotic surgery readiness), and Billings Clinic's deployment of clinical‑grade AI has cut medication‑reconciliation burden by hundreds of hours (DrFirst case study: Billings Clinic clinical‑grade AI medication reconciliation savings).

“The medication history system saves the team nearly 1 million clicks every year.”

Key local metrics:

Program	Metric	Value
St. Vincent	Planned beds	243 (+16 expansion)
Billings Clinic	Organization size	336 beds; Level‑9 digital health
DrFirst AI	Staff time saved	~300+ hours / ~1M clicks

For health leaders and clinicians, targeted training like Nucamp AI Essentials for Work 15‑week bootcamp registration can fast‑track prompt‑writing and pilot design to move from proof‑of‑concept to measurable ROI while meeting compliance and staffing constraints.

We selected Billings‑focused AI prompts and use cases by applying a pragmatic, compliance‑first methodology that balances clinical impact, operational ROI, and rural readiness: we started with Qure.ai's five‑point deployment framework to align pilots with clinical workflow and IT integration (Qure.ai five-point guide to AI deployment in healthcare), prioritized pilots that show measurable KPIs (e.g., appointment confirmations and no‑show reductions demonstrated in the Voiceoc Allure Medical rollout) and required vendor BAAs and validated security controls, and flagged conversational and triage agents for extra privacy review based on documented HIPAA chatbot risks (HIPAA compliance risks for AI chatbots (PMC study)).

We favored low‑risk/high‑value starting points (administrative automation, imaging assist, predictive alerts), mandated local IT testing and clinician training, and selected vendors with proven outcomes such as Voiceoc's automation gains to ensure pilots scale to system‑level savings (Voiceoc Allure Medical appointment automation case study).

Selection criteria used in Billings pilots are summarized below.

Criterion	Why it matters
Clinical alignment	Minimizes workflow disruption
Governance & HIPAA	Protects PHI, vendor BAAs required
IT integration	Interoperability with EHRs and bandwidth limits
Training & change management	Adoption and safety
Testing & ROI measurement	Pilot validation and scale decision

Medical imaging is a high‑value starting point for Billings health systems because AI can triage workload, reduce unnecessary follow‑ups, and preserve clinician oversight where it matters most; Google DeepMind's CoDoC research shows a practical way to “know when the AI doesn't know,” deciding when to accept an AI reading or defer to a radiologist and reducing false positives by 25% without missing true positives - advantages that translate directly to rural and regional settings like Billings by lowering unnecessary callbacks and conserving specialist time (DeepMind CoDoC reliable AI tools for healthcare research).

CoDoC is designed to work as an add‑on to existing, even proprietary, imaging models with only a few hundred labelled cases required for calibration, making pilots feasible for Billings Clinic or the new St.

Vincent replacement without re‑engineering core vendors' algorithms; local leaders should pair such pilots with revenue‑cycle and compliance plans to capture ROI and meet Montana privacy rules, as outlined in our local playbooks for cost and compliance (How AI Is Helping Healthcare Companies in Billings Cut Costs and Improve Efficiency - local playbook) and the 2025 implementation guide (Complete guide to using AI in Billings (2025) implementation guide).

Key CoDoC trial outcomes for imaging pilots:

Metric	Result
False positives (mammography)	−25%
Missed true positives	0
Clinician reading workload	Reduced by ~66%

For Billings health systems pursuing precision oncology and faster drug discovery, IBM's enterprise AI work shows practical paths: IBM notes AI is already used “for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals,” and those capabilities can be repurposed locally to improve treatment matching and accelerate early‑phase drug candidate selection (IBM AI for healthcare benefits overview).

Industry examples include Pfizer using IBM's machine learning tools to select stronger clinical‑trial candidates, a workflow Billings clinics could adapt to boost trial enrollment and reduce screening burdens in rural catchments (IBM machine learning clinical trial case study with Pfizer).

IBM's evolution into watsonx also gives smaller systems the tooling to train or tune models on local genomic and EHR data while retaining vendor governance and audit trails (IBM watsonx enterprise AI platform for personalized medicine).

“For anyone receiving the diagnosis, or supporting a loved one through it, cancer can be overwhelming.”

Watson capability	Clinical role
Watson for Oncology	Evidence‑based treatment recommendations
Watson for Genomics	Somatic mutation interpretation
Clinical Trial Matching	Automated eligibility and referrals

Local leaders should start with IRB‑aware pilots, business associate agreements (BAAs), and measurable KPIs (time‑to‑match, enrollment rate, and clinician review time) to make personalized medicine achievable in Billings.

Virtual assistants and chatbots are a practical first AI pilot for Billings dermatology clinics because they automate bookings, triage FAQs, reduce no‑shows and free front‑desk staff for clinical work - Voiceoc's dermatology playbook shows how 24/7 conversational AI handles appointment scheduling, multilingual messaging, follow‑ups and EHR integration to capture after‑hours leads and speed responses (Voiceoc 24/7 AI virtual assistant for dermatology clinics - appointment automation).

For Montana practices with tight staffing and rural patient flows, a phased pilot with BAAs, local IT testing and simple KPIs (bookings, response time, revenue uplift) is advisable; reported vendor outcomes from real rollouts (used as selection examples) are summarized in the Allure Medical case study (Voiceoc Allure Medical appointment automation case study) and local compliance and pilot playbooks for Billings explain HIPAA and state requirements (Guide to using AI in Billings healthcare (2025 compliance and pilot playbook)).

“The medication history system saves the team nearly 1 million clicks every year.”

Metric	Reported result
Appointment bookings	+35–50%
Response time	−40%
Front desk workload	−Up to 60%

As Billings readies new ORs and robotic infrastructure (Intermountain's St. Vincent replacement and Billings Clinic's digital initiatives), local surgical leaders should weigh the growing evidence for robotic‑assisted systems: Intuitive's registry and outcomes summaries compile thousands of comparative studies showing consistent perioperative advantages for the da Vinci platform, especially for complex pelvic and thoracic cases (Intuitive da Vinci research and outcomes).

The large COMPARE meta‑analysis (Annals of Surgery, 2025) pooled >1.1M da Vinci cases and found tradeoffs - longer operative times but fewer conversions, lower blood loss and transfusions, shorter hospital stays, and reduced 30‑day complications and mortality versus laparoscopic or open approaches (COMPARE meta-analysis Annals of Surgery 2025).

Key pooled perioperative outcomes to discuss with Billings hospital CFOs and surgical committees are summarized below. For implementation, clinicians should heed balanced critiques about training, cost and learning curves: the AMA Journal of Ethics review recommends structured residency/proctoring pathways, competency metrics, and pilot KPIs so local programs convert clinical promise into safer, equitable access for Montana patients (AMA Journal of Ethics review on robotic-assisted surgery).

Metric	dV‑RAS vs lap/VATS	dV‑RAS vs open
Operative time (MD)	+17.7 min	+40.9 min
Length of stay (MD)	−0.51 days	−1.85 days
Conversion to open (OR)	0.44	-
30‑day complications (OR)	0.90	0.56

Billings health systems should prioritize predictive analytics that catch sepsis and broader infectious threats earlier so clinicians can act before rapid deterioration: Johns Hopkins' Targeted Real‑Time Early Warning System (TREWS) detected sepsis up to ~6 hours sooner in severe cases, was used by 4,000+ clinicians across multi‑site studies and is associated with roughly a 18–20% reduction in mortality - making it a practical bedside tool for local pilots in Billings Clinic and the new St.

Vincent replacement (Johns Hopkins TREWS sepsis AI detection study). Complementary FDA‑authorized tools such as the Sepsis ImmunoScore add validated, regulatory‑cleared options for EHR integration and clinical decision support, reducing time‑to‑treatment in emergency workflows (NEJM AI validation of FDA‑authorized Sepsis ImmunoScore).

At the county and state level, BlueDot‑style biosurveillance - pairing local EHR signal detection with travel and syndromic data - can give Montana public health leaders earlier warning of emerging clusters and help prioritize scarce rural resources, as reviewed in practical overviews of sepsis and AI deployment strategies (Mayo Clinic Platform overview: using AI to predict sepsis onset).

“It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved.”

Metric	Result
Earlier detection (severe cases)	~6 hours
Mortality reduction (deployment studies)	18–20%
Time to first antibiotic (when confirmed ≤3 hr)	−1.85 hours

Together these tools form a layered early‑warning strategy Billings can pilot with BAAs, local IT/EHR testing, and KPIs (time‑to‑antibiotic, sepsis mortality, alert confirmation rate) to balance sensitivity, alert fatigue, and rural resource constraints.

Administrative automation can deliver fast, measurable savings for Billings health systems by reducing front‑desk burdens, accelerating claims throughput, and freeing clinicians to focus on care; platforms like Olive automate repetitive RCM tasks while Nuance DAX and ambient documentation tools reduce documentation time at the point of care, both of which are now commonly listed among Becker's 2024 revenue cycle management companies list (Becker's 2024 revenue cycle management companies list).

RPA is the Trojan Horse sneaking AI workflows into hospitals, bypassing traditional API bottlenecks and vendor inertia.

Practical pilots in Billings should combine vendor BAA reviews, small‑scope RPA pilots, and EHR integration testing so local teams capture ROI without disrupting workflows - the technology pattern is well explained in the AI‑driven RPA EHR integration guide from Topflight Apps (AI-driven RPA EHR integration guide from Topflight Apps).

For sourcing and procurement, Olive provides turnkey sourcing and vendor comparison features that shorten vendor selection time and standardize requirements across stakeholders (see Olive AI revenue cycle automation solutions: Olive AI revenue cycle automation solutions).

Factor	APIs	AI‑driven RPA
Speed to deploy	Months	Weeks
Typical outcome	Structured data exchange	Automated UI tasks (eligibility, claims)
U.S. adoption	-	~74% of hospitals use automation

Start with a 30–90 day Olive‑sourced vendor pilot and one RPA bot for eligibility/denials to quantify savings, then add ambient documentation (Nuance DAX) pilots tied to time‑saved KPIs and HIPAA governance for Billings Clinic and the new St.

Vincent replacement project.

Remote patient monitoring in Billings can turn widespread consumer wearables into actionable clinic signals for atrial fibrillation (AF) screening and chronic care when paired with clinical RPM platforms like Biofourmis that manage alerts, workflows, and follow‑up.

Large studies show promise but also important limits: the Stanford Apple Heart Study enrolled >400,000 and demonstrated that smartwatch pulse notifications identify AF with a positive predictive value around 84% and lead many users to seek care, while specialty reviews summarize high sensitivity/specificity for AF but caution about false positives, intermittent wear, and workflow burden for clinicians.

For local pilots, start with clear pretest populations (older adults, post‑stroke clinics, HF cohorts), BAAs and telehealth pathways so alerts convert to timely ECG confirmation and anticoagulation decisions rather than unnecessary ED visits.

Key trial metrics are summarized below to guide pilot KPIs and sample sizes.

Metric	Result
Apple Heart Study enrollment	>400,000
Irregular pulse notifications	0.52%
PPV of notification for AF	~84%
Percentage with AF on follow‑up patch	34%
WATCH AF trial (sensitivity / specificity)	93.7% / 98.2%

“My watch thinks I have Atrial Fibrillation!”

Use these findings to design Billings‑specific pilots that measure notification confirmation rate, time‑to‑ECG, downstream anticoagulation rate, and clinician workload; see the Stanford Apple Heart Study AF detection findings (Stanford Apple Heart Study AF detection findings), the ACC expert review on smartwatch detection of atrial fibrillation (ACC 2024 expert review of smartwatches and atrial fibrillation), and the NUEM summary providing WATCH AF trial context (NUEM summary and WATCH AF trial context).

For Billings providers facing workforce shortages and long waits, evidence shows conversational agents can expand access to low‑intensity mental health support while preserving clinical escalation: the original Woebot randomized controlled trial found a moderate reduction in depressive symptoms (PHQ‑9 between‑group d≈0.44), high engagement (mean ~12 interactions over 2 weeks) and markedly lower attrition versus an information control (Woebot randomized controlled trial JMIR 2017 showing reduction in depressive symptoms); broader syntheses report small‑to‑moderate pooled effects for therapy chatbots while underscoring short‑term benefits and the need for safety monitoring (Systematic review of AI‑CBT chatbots (PMC) summarizing pooled effects and safety considerations), and a 2024 RCT replicates short‑course improvements in subclinical young adults supporting feasibility for brief pilots (2024 therapy chatbot randomized trial JMIR Formative Research demonstrating short‑course improvements).

For Billings we recommend starting with BAAs, HIPAA‑reviewed vendors, clear escalation paths to local clinicians, and KPIs that include PHQ‑9 change, engagement, attrition and referral conversion; a short pilot in primary care, student health, or behavioral telehealth can rapidly show whether chatbots safely close access gaps.

Key trial metrics to track locally are summarized below.

Metric	Result
PHQ‑9 effect (Woebot)	d = 0.44 (2‑week RCT)
Mean engagement	~12 interactions / 2 weeks
Attrition (Woebot vs control)	9% vs 31%

Genomics and clinical decision support are high‑leverage areas for Billings health systems: Deep Genomics' AI platform uses RNA‑first models to prioritize disease mechanisms and candidate therapeutics, enabling smaller hospitals to get more actionable signals from limited local sequencing and EHR data - an approach summarized on the Deep Genomics AI platform for RNA therapeutics (Deep Genomics AI platform for RNA therapeutics) and the Deep Genomics company overview (Deep Genomics company overview).

Complementary tools like Google's DeepVariant produce highly accurate variant calls that feed clinical decision support, while reviews of AI‑powered precision medicine show how genetic‑risk optimization and interpretation layers improve screening and treatment matching in resource‑constrained settings (NAR Genomics review of AI‑powered precision medicine).

For Billings pilots prioritize BAAs/IRB workflows, vendor validation on local variant frequencies, on‑site or partnered sequencing logistics, and KPIs such as turnaround time, diagnostic yield, and actionable therapy rate; start with tumor panels or rare‑disease trios, embed genomics reports into EHR CDS pathways, and plan clinician training so interpretation scales safely from pilot to systemwide care.

For Billings' stretched emergency systems, AI‑assisted dispatch and triage - like Corti's real‑time automatic speech recognition (ASR) and out‑of‑hospital cardiac arrest (OHCA) detectors and machine‑learning text‑analysis of emergency calls - offer concrete ways to detect critical patients faster and prioritize scarce ambulances and ED beds.

Studies reviewed in the JMIR AI in Emergency Medicine review show Corti's end‑to‑end models can extract location, guide questions, and recognize OHCA faster than dispatchers, while a separate JMIR model development study demonstrates that text‑mining of call transcripts can predict severely injured patients for earlier resource allocation (JMIR review of AI in emergency medicine (2023), EMD severe‑injury text‑analysis study (JMIR 2022)).

For Montana pilots, pair a small‑scope dispatch integration with EHR linkage, vendor BAAs, bias‑testing, and clinician training; monitor time‑to‑recognition, triage accuracy, and false‑alarm rates to avoid automation complacency.

Local compliance and pilot templates (BAA, HIPAA review, KPIs) help accelerate safe deployments in Billings' rural context (Billings AI compliance and pilot playbook (2025)).

Key comparison points are summarized below.

System	Use case	Reported outcome
Corti ASR/OHCA	Dispatcher assistance, OHCA detection	Faster OHCA recognition (retrospective study)
ML call text‑analysis	Predict severe injury for triage	Validated model development & retrospective validation

Conclusion - Billings healthcare leaders should convert the prioritized use cases above into short, measurable pilots that protect patient data, prove ROI, and build local capacity: require vendor BAAs and follow the 2025 Hospital Pilot Playbook HIPAA compliance guide for healthcare pilots (2025 Hospital Pilot Playbook HIPAA compliance guide for healthcare pilots), embed telehealth workflows and escalation paths using the AMA Telehealth Integration Toolkit for safe virtual care workflows (AMA Telehealth Integration Toolkit for safe virtual care workflows), and invest in practical staff training - prompt writing, vendor testing, and KPI design - via cohort courses like Nucamp's AI Essentials for Work to shorten the path from proof‑of‑concept to scale (Nucamp AI Essentials for Work 15‑week bootcamp registration (AI for work)).

Prioritize 30–90 day pilots with clinician oversight, a BAA, bias testing, and clear KPIs; capture wins (reduced time‑to‑antibiotic, booking lifts, and back‑office hours) and use them to justify scale.

“The medication history system saves the team nearly 1 million clicks every year.”

Below are example pilot KPIs to track locally:

Pilot	KPI	Target
Sepsis early warning	Time‑to‑first antibiotic	−1.5 to −1.85 hrs
Virtual assistant	Appointment bookings	+35–50%
RCM automation	Staff time saved	~300+ hours / yr