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

Hospitals and clinics across Tennessee - including Murfreesboro practices - are already using prompt-based AI to trim paperwork and improve patient conversations: a practical catalog like Paubox's Paubox 100+ ChatGPT prompts for healthcare professionals supplies ready-made templates for patient messages, documentation, and telemedicine workflows, while Vanderbilt's study shows AI prompts can help patients write clearer portal questions and cut the back-and-forth that wastes clinician time (Vanderbilt University Medical Center study on AI prompts and patient communication); combined with AI note-taking that frees doctors to listen more to patients, these tactics translate into measurable minutes saved per visit - exactly the skillset taught in Nucamp's Nucamp AI Essentials for Work bootcamp (AI at Work: Foundations, Writing AI Prompts, Job-Based Practical AI Skills) - 15-week training, which trains staff to write effective prompts and deploy them safely in clinical settings.

Program	Length	Early-bird Cost	Courses Included
Nucamp AI Essentials for Work bootcamp - AI at Work training	15 Weeks	$3,582	AI at Work: Foundations; Writing AI Prompts; Job-Based Practical AI Skills

Selection prioritized prompts that are specific, auditable, and immediately usable in Tennessee clinical workflows: candidates were mined from clinical prompt-engineering literature and industry guidance, filtered for Murfreesboro-relevant use cases (administrative automation such as payer-provider alignment, plus high-impact clinical tasks), and validated through an iterative loop of clinician review and simulated scenarios.

Core criteria came from the JMIR tutorial on prompt engineering as a growing medical skill - favoring prompts that support decision support and clear instructions (Prompt engineering tutorial for clinical decision support - JMIR) - and HealthTech's practical checklist that demands specificity, output format, and follow-up prompts for safe deployment (Prompt engineering best practices for healthcare deployment - HealthTech).

Local relevance was confirmed against Murfreesboro pilot descriptions for authorization and billing workflows to ensure administrative prompts cut real clinician steps (AI Essentials for Work syllabus - payer-provider alignment use cases (Nucamp)), and final candidates were stress‑tested using agentic simulation methods before inclusion.

Source	Year	Key relevance
Prompt Engineering as an Important Emerging Skill (JMIR)	2023	Decision-support framing; clinical validation criteria
Prompt Engineering in Healthcare: Best Practices (HealthTech)	2025	Specificity, output format, iterative testing
From prompt to platform (Advances in Simulation)	2025	Simulation-driven validation of prompts

“The more specific we can be, the less we leave the LLM to infer what to do in a way that might be surprising for the end user.” - Jason Kim

Diagnostic support prompts for cardiology and general medicine can standardize initial triage in Murfreesboro clinics by translating symptoms and raw test results into clear next steps: for arrhythmia workups a prompt can summarize a 12‑lead ECG and recommend ambulatory monitoring (Holter or event recorder) or an electrophysiology study when rhythm–symptom correlation is lacking, reflecting Mayo Clinic guidance on ECG, Holter, event recorders, echocardiography and EP testing (Mayo Clinic guidance on heart arrhythmia diagnosis and treatment); prompts can also require explicit symptom‑rhythm correlation before labeling sinus‑node dysfunction, per clinical guidance on diagnostic workup (Medscape review of sinus node dysfunction diagnostic workup).

For suspected heart failure, embed natriuretic‑peptide thresholds from validated clinical‑decision rules so a prompt flags patients for echocardiography (example referral cutoffs: BNP and NT‑proBNP ranges used to decide echo referral in the CDR), aligning with best‑pathway evidence that BNP testing plus ECG helps target echocardiography efficiently (European Cardiology Review on diagnosing heart failure and best pathways).

The practical payoff: consistent prompts reduce missed referrals and make triage defensible at the point of care.

Image-driven radiology prompts can help Murfreesboro imaging centers turn chest CT series into consistent, auditable comparisons by automating the matching of pulmonary nodules across follow‑up exams: a recent study demonstrates an AI algorithm that enables precise automated matching of pulmonary nodules on follow‑up chest CT, providing a solid basis for standardized and accurate evaluations (AI-based automated matching of pulmonary nodules - European Radiology Experimental (study)).

Practical prompts for local PACS/RIS integration should extract nodule descriptors (location, diameter, scan date), link to the prior best-matching study, and generate a structured comparison table for the radiology report so radiologists and referring clinicians see growth or stability at a glance.

Pairing these image-driven prompts with Murfreesboro-focused AI workflows - already discussed in local pilots and guides - makes serial nodule surveillance more consistent across clinics and reduces the chance of missed interval change or unnecessary repeat imaging (Nucamp AI Essentials for Work bootcamp - How AI helps healthcare companies in Murfreesboro).

Item	Detail
Article	Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT
Journal / Date	European Radiology Experimental - 02 May 2025
Key finding	Algorithm enables precise follow-up matching of pulmonary nodules, supporting standardized and accurate evaluations

Personalized treatment planning for Murfreesboro clinics can now pair Mayo Clinic's pragmatic approaches to pharmacogenomic alerts with medicine‑tuned LLMs such as Google's Med‑PaLM 2 to generate concise, actionable recommendations at the point of prescribing: Mayo researchers tested AI‑enhanced designs and found clinicians preferred concise, individualized alerts (for example in citalopram prescribing) because they are more usable and less likely to contribute to alert overload (Mayo Clinic AI-enhanced pharmacogenomic alerts study), while Google's Med‑PaLM 2 - piloted at Mayo Clinic and designed for the medical domain - has shown strong exam performance and the ability to synthesize records and images but requires careful guardrails and encrypted data controls for safe deployment (Fortune coverage of Google Med‑PaLM 2 Mayo partnership, Google Cloud blog on Med‑PaLM 2 medical LLM).

The practical payoff for Tennessee practices: a short, well‑structured AI alert that links a patient's genotype, current meds, and a one‑line next step can make precision prescribing feasible during a 15‑minute visit.

Study / Tool	Key point	Relevance to Murfreesboro
Mayo Clinic (Clinical & Translational Science, 2024)	Clinicians favored concise, individualized pharmacogenomic alerts for citalopram	Design templates that reduce alert overload and support point-of-care decisions
Med‑PaLM 2 (Google Cloud / pilots)	Medicine‑aligned LLM with strong exam performance; pilot deployments use encrypted customer data and emphasize guardrails	Can synthesize EHR/imaging/genomics into draft plans but needs local validation and privacy controls

“Finding the right balance of detail is crucial. Our study indicates that a one-size-fits-all approach to alerts is not effective. We need to consider who is reading the alert and what they need in that moment.” - Caroline Grant

Clinical documentation and smart EHR summarization in Murfreesboro clinics becomes practical when AI prompts are embedded into Epic or Cerner workflows to produce auditable, FHIR‑structured visit summaries that populate clinician tools (for example Epic Hyperspace/NoteWriter or Cerner Millennium) and reduce after‑visit charting friction; vendors and integrators recommend using SMART on FHIR and OAuth2 flows with middleware so prompts can call out discrete fields (problems, meds, follow‑ups, billing codes) and return both a human‑readable one‑line assessment plus a coded FHIR bundle for the chart - see a detailed Cerner vs Epic feature and AI roadmap comparison (Cerner vs Epic: 2025 comparison) and a practical developer roadmap for connecting apps via APIs and FHIR (How to integrate your EHR with Cerner or Epic); local Murfreesboro pilots should prioritize role‑based templates, tokenized PHI handling, and endpoint testing in vendor sandboxes to keep notes concise, auditable, and billable while improving sign‑out clarity for cross‑clinic referrals (Epic integration strategies & best practices).

EHR	Approx. U.S. acute care market share	Integration / API notes
Epic	~42.3%	App Orchard, Care Everywhere, SMART on FHIR; deep integrated UI (Hyperspace/NoteWriter)
Cerner (Oracle Health)	~22.9%	Cerner Ignite APIs, CommonWell; modular Millennium platform with strong interoperability

“Getting EHRs to work smoothly with APIs isn't always straightforward... with the right approach and a rigorously selected health tech partner... you can avoid major headaches and get Oracle Cerner or Epic EHR integration done efficiently.” - Andrew G., Senior Software Engineer at TATEEDA

In Murfreesboro clinics, administrative AI prompts can cut denials and speed reimbursements by automating CPT/ICD‑10 selection, building pre-filled templates for routine visits and diagnostics, and running code‑scrubbing checks before claims submit; practical recipes include templated CPT/ICD pairings for common internal‑medicine encounters, modifier prompts (‑25, ‑95) tied to documented time or MDM, and automated appeal workflows that prepare clean resubmissions and insurer‑call scripts.

Pairing those prompts with insurer‑call automation reduces time staff spend on hold and makes “refile, refile, refile” work repeatable rather than manual, while integrating CPT/ICD mappings into privileging and EHR connectors gives real‑time visibility for credentialing and billing parity.

Start with pilot templates for the highest‑volume codes, track claim acceptance and time‑saved KPIs, and reuse successful prompt templates across Epic/Cerner workflows; see practical how‑tos on how to guide to automate coding and billing tasks with AI automation, coding tips for superbills and out‑of‑network claims at CPT and ICD‑10 coding tips for superbills and out‑of‑network claims, and local payer‑provider alignment pilots that show where automation most quickly reduces administrative load in Murfreesboro at Murfreesboro payer‑provider alignment pilots for healthcare automation.

The payoff is concrete: fewer miscoded rejections, less time on repetitive appeals, and measurable staff hours reclaimed for patient care.

Metric	Value / Source
Average physician admin time per week	15.5 hours - GetMagical
Claims with inaccuracies	~80% - GetMagical
Reported time saved with automation	~7 hours/week - GetMagical
Estimated U.S. loss from billing errors	$935 million weekly - GetMagical

Virtual health assistants modeled on OSF HealthCare's “Clare” show how prompt-driven chat and symptom workflows can act as a 24/7 digital front door that diverts nonurgent contacts, speeds scheduling, and preserves nursing capacity - OSF's pilots paired a Fabric-powered assistant (Clare) with contact‑center prompts that produced a reported $2.4M+ ROI and $1.2M in contact‑center cost avoidance while serving as a single point of contact for navigation and scheduling (Fabric case study on Clare and OSF HealthCare).

Complementary nurse‑facing prompts and structured triage screens (SymptomScreen) drove striking call‑center improvements - an 86% decrease in average time to answer for nurse triage (from 8:51 to 1:12), a 69% drop in abandoned calls, and an 11% reduction in cost per contact - allowing nurses to focus on higher‑acuity care instead of clerical tasks (SymptomScreen case study on OSF results).

For Murfreesboro clinics, emulate this stack by building concise symptom‑navigation prompts, routing rules, and audit‑ready handoffs so local practices can reduce urgent‑care leakage and make staffing decisions defensible at the point of contact.

Metric	Reported Result
Average time to answer (nurse triage)	Reduced 86% (8:51 → 1:12) - SymptomScreen
Abandoned calls (nurse triage)	Reduced 69% (23.8% → 7.5%) - SymptomScreen
Cost per contact	Reduced 11% - SymptomScreen
Recognized ROI	$2.4M+ (one year) - Fabric / OSF case study
Contact center cost avoidance / new patient revenue	$1.2M / $1.2M - Fabric case study

“The implementation of SymptomScreen has allowed the nurses to work at the top of their licensure by having them focus on higher acuity triages. This has positively impacted the overall satisfaction and retention of the nursing staff.” - Ashley R Chitwood MSN, RN, NE-BC, Director, Digital Outpatient and Community Care

Predictive‑analytics prompts for risk stratification can turn Murfreesboro EHR data into actionable care tiers by using validated toolkits and hospital‑calibrated models: Johns Hopkins' ACG System offers population‑and patient‑level scores that flag likely future costs, hospitalizations, readmissions, and high‑utilizer cohorts - outputs a community clinic can ingest to queue care‑manager outreach or remote monitoring - and Johns Hopkins' inHealth/PMAP work demonstrates how precision‑medicine platforms and cloud analytics (Azure) support secure, scalable model deployment for regional health systems (Johns Hopkins ACG System overview, Johns Hopkins inHealth and PMAP precision medicine platform).

Practical prompt recipes for Murfreesboro: (1) generate a ranked risk roster of top 5% high‑utilizers with suggested interventions, (2) create dynamic readmission‑risk summaries for discharge planners, and (3) surface near‑term critical‑care risks calibrated to local casemix - an approach supported by Hopkins ML work that showed delirium models can predict ICU delirium with very high accuracy when tuned to hospital data (Johns Hopkins ICU delirium prediction models).

The payoff in Murfreesboro: targeted outreach and monitoring triggered by a prompt can shift scarce care‑manager time from broad outreach to the handful of patients most likely to benefit now.

Model / Platform	Key output / benefit
Johns Hopkins ACG System	Population & patient risk scores for cost, hospitalization, readmission, high‑utilizer identification
Johns Hopkins inHealth (PMAP)	Precision medicine analytics platform for secure, scalable model deployment (cloud/on‑prem)
Hopkins ICU delirium ML models	Dynamic/static risk prediction to allocate ICU prevention interventions (high accuracy when hospital‑calibrated)

“Being able to differentiate between patients at low and high risk of delirium is incredibly important in the ICU because it enables us to devote more resources toward interventions in the high-risk population.” - Robert Stevens, MD

Real-time perioperative monitoring prompts for Murfreesboro operating rooms translate continuous vital-sign streams into actionable alerts that follow international consensus: prompt rules should flag mean arterial pressure (MAP) breaches (for example, sustained drops toward the 60–70 mm Hg range) because low intraoperative MAP is associated with myocardial injury, acute kidney injury, and death, and guidelines now recommend continuous arterial‐pressure monitoring to reduce the severity and duration of hypotension (POQI international consensus on perioperative blood pressure management (2024), APSF recommendations for hemodynamic instability in perioperative patients).

Practical prompt templates for Tennessee ORs should (1) set patient‑specific MAP thresholds, (2) require a short differential of likely causes (hypovolemia, vasodilation, cardiogenic), and (3) push a time‑stamped escalation action (fluid bolus, vasopressor start, anesthesiologist notification) into the intraoperative dashboard so teams can close the loop quickly; these same recipes power local pilot playbooks for adopting AI‑driven workflows in Murfreesboro care settings (AI Essentials for Work bootcamp syllabus at Nucamp).

The practical payoff is immediate: a concise, time‑stamped prompt that alerts on MAP <65 mm Hg makes escalation auditable and shortens the window when organs are at risk.

Item	Prompt recommendation
MAP threshold	Flag sustained MAP ≤65 mm Hg (note: harms increase in ~60–70 mm Hg range)
Monitoring mode	Consider continuous arterial pressure monitoring to reduce hypotension duration
Escalation action	Require time‑stamped cause list + one immediate recommended intervention

Behavioral health providers in Murfreesboro should embed short, validated screening prompts into intake and triage workflows so busy clinics reliably catch depression and suicide risk: start with the two‑item PHQ‑2 as a “first‑step” depression screener (asks about interest/pleasure and depressed mood over the past two weeks and uses a score cutpoint of 3 to trigger further assessment) - see the PHQ‑2 tool and scoring details (PHQ‑2 depression screener and scoring details); if positive, prompt an automated PHQ‑9 follow‑up to quantify severity and guide next steps (PHQ‑9 severity assessment and guidance).

Always pair depression screening with a rapid suicide screen: the Ask Suicide‑Screening Questions (ASQ) toolkit is a validated four‑question, ~20‑second tool that requires a brief suicide‑safety assessment by a trained clinician for any “yes” response (Ask Suicide‑Screening Questions (ASQ) toolkit and materials).

The practical payoff: a two‑question PHQ‑2 plus a 20‑second ASQ embedded as EHR prompts lets staff triage risk during routine visits and automatically queue a clinician‑led safety assessment when needed, reducing missed cases and making follow‑up actions auditable.

Tool	Items / Time	Trigger / Next step
PHQ‑2	2 items (depressed mood, anhedonia)	Score ≥3 → administer PHQ‑9
PHQ‑9	9 items (severity assessment)	Use clinician evaluation/diagnostic instruments to determine depressive disorder
ASQ	4 yes/no questions (~20 seconds)	Any “yes” → brief suicide safety assessment (BSSA) by trained clinician

For Murfreesboro clinics managing diabetes and heart‑failure populations, practical remote‑monitoring prompts turn constant device streams into clear, auditable actions: ingest CGM and wearable feeds (modern CGMs can produce up to 288 readings per day), summarize 24‑hour trends, flag rapid excursions or prolonged hypoglycemia, and auto‑queue a clinician task or care‑manager outreach with one recommended intervention and urgency level - so that busy teams see “what to do now” instead of raw data.

Pair diabetes prompts with medication‑adherence and activity overlays so clinicians can spot behavioral drivers of glucose swings, and reuse the same pattern for heart‑failure RPM by surfacing trend alerts (weight/shortness‑of‑breath flags, rising diuretic needs) to prevent readmissions; these workflows reflect the documented RPM benefits of real‑time intervention and reduced hospitalizations (Diabetes and Remote Patient Monitoring - Binariks: RPM benefits and diabetes monitoring).

Implement with clinician‑facing dashboards that unify CGM, insulin and activity data (for example, the Enhance‑d Dashboard) so Murfreesboro care teams get retrospective and near‑real‑time context for tailored outreach (Enhance‑d clinician dashboard: integrated glucose and activity review), and start by piloting wearable‑first prompts that reflect the latest device trends for 2025 to speed adoption and patient engagement (Managing Diabetes with Wearable Technology Trends for 2025 - Smiles Medical Supply).

Tool / Source	Key point for Murfreesboro RPM
Smiles Medical Supply (2025)	CGM/insulin‑pump trends enable continuous, smartphone‑integrated monitoring
Binariks (RPM overview)	RPM improves engagement, enables timely interventions, and can reduce hospitalizations
Enhance‑d Dashboard	Clinician‑focused integration of glucose, insulin, activity for retrospective and near‑real‑time review

Getting started in Murfreesboro means first choosing a narrow, measurable pilot (for example: a triage prompt, an EHR visit‑summary prompt, and a billing‑scrub prompt), then following prompt‑engineering best practices - be specific about output format, include context, and iterate with clinician feedback - so your team can validate accuracy in a vendor sandbox before live use (Prompt engineering best practices in healthcare - HealthTech Magazine).

Use ready templates to speed adoption (Paubox's Paubox 100+ ChatGPT prompts for healthcare professionals is a practical starter set), require auditable outputs (source tags or FHIR bundles), and track simple KPIs - hours reclaimed, claim rejections, and time‑to‑answer - before scaling.

For staff training and hands‑on prompt writing, consider Nucamp's focused 15‑week AI Essentials for Work curriculum that teaches how to write, test, and deploy safe prompts in clinical workflows (Nucamp AI Essentials for Work syllabus & registration (15‑week AI for Work)); the concrete benefit: three validated pilot prompts can convert abstract AI potential into repeatable minutes saved and clearer, auditable care actions for Tennessee clinics.

Program	Length	Early‑bird Cost	Learn More / Register
Nucamp AI Essentials for Work	15 Weeks	$3,582	Nucamp AI Essentials for Work syllabus & registration

“The more specific we can be, the less we leave the LLM to infer what to do in a way that might be surprising for the end user.” - Jason Kim