Combining Direct and Indirect Metrics for a Holistic Approach to AI Performance Evaluation in Business Applications

Artificial intelligence (AI) is used more and more in businesses, including healthcare. People who run medical offices in the United States are starting to see how AI can make work easier, help patients, and lower costs. But it is hard to measure how well AI works because AI systems do many different things. The best way to check AI’s performance is to use both direct and indirect measurements.

Direct Metrics

Direct metrics look at the technical side of how AI models work. These include:

• Precision and Recall: These show how accurate AI predictions are and how complete they are. This is important when AI tries to find patient symptoms or sort phone calls in automated systems.
• F1 Score: This combines precision and recall into one number.
• Mean Squared Error (MSE) or Root Mean Squared Error (RMSE): These measure how much AI’s guesses differ from real answers, useful for predicting things like appointment numbers or how long calls take.
• Perplexity: This shows how well language models can predict text that sounds like a person talking. This helps with AI answering patient questions.
• Accuracy, Model Loss, and Area Under Curve – Receiver Operating Characteristic (AUC-ROC): These also show how well AI is doing its tasks.

In healthcare, it is very important that AI gives exact and correct results. Mistakes in scheduling, billing, or answering phone questions can cause problems.

Indirect Metrics

Indirect metrics focus on the business and user side, not just technical accuracy. In healthcare, these include:

• Customer satisfaction scores, which tell how patients feel about AI answering phones or sending reminders.
• User engagement rates, such as the number of calls finished without needing a person.
• Operational efficiency, like shorter wait times on calls and less work for staff.
• Revenue growth or saving money from fewer missed appointments or better use of resources thanks to AI.
• Employee productivity, showing how AI helps staff focus on more important work.

Using both direct and indirect metrics gives medical administrators a complete understanding of AI’s value for their business.

Why Combining Metrics Matters to US Healthcare Practices

Medical office managers and IT workers in the US face special challenges when setting up AI systems, especially for phone automation and answering services like Simbo AI. Patient communication rules, privacy laws like HIPAA, and many different health questions mean AI must be technically strong and good at dealing with patients.

Technical accuracy alone is not enough. For example, if a language model is good at predicting text but doesn’t help patients or make calls faster, it is not very useful. On the other hand, a system that helps workflows but makes mistakes might annoy patients.

Healthcare managers get more benefits when they track both AI accuracy and business results, such as:

• First contact resolution rate: Can AI answer patient questions right away without passing them on?
• Mean time to fix errors: How quickly do they fix AI mistakes?
• Patient retention and new patient growth: Does AI help keep patients and attract new ones?

Jerald Murphy, an AI expert with over 30 years of experience, says tracking both technical measures and real-world effects is important. This balanced view shows if AI really helps or not.

Key Performance Indicators (KPIs) for AI Success in Healthcare

To use AI well in healthcare, clear KPIs matched to healthcare work are needed. Here are examples medical offices can use:

• Mean Squared Error (MSE): Measures how close AI predictions are, like predicting call volumes or appointment times.
• Perplexity: Checks how natural AI sounds when answering phones, important for patient conversations.
• First Contact Resolution (FCR) Rate: Shows how many calls are solved on the first try. This matters for patient happiness and efficiency.
• Call Abandonment Rate: The percentage of callers who hang up before getting help. Lower rates mean AI responds better.
• Average Handling Time (AHT): The time AI spends on calls compared to humans. Shorter times usually mean better AI performance.
• Patient Satisfaction Scores: From surveys asking patients about their experience with AI phone systems or reminders.
• Operational Cost Savings: Money saved by needing fewer staff for routine calls or desk work.
• AI Adoption Rate Among Staff: How many workers are using AI tools regularly, showing if AI fits well in daily work.

Tracking both direct (technical) and indirect (business) KPIs helps leaders see if AI works well on paper and in real life.

AI and Workflow Efficiency in Healthcare Front-Office Operations

In US medical offices, using AI for front-office phone work is important because of many calls and the need for fast, accurate answers. AI answering services can handle appointment bookings, prescription refills, insurance questions, and general patient queries without needing a person.

AI helps by doing repetitive tasks so front desk workers can focus on harder patient needs or organizing care. This improves how well the office works.

For example, studies by Devoteam show AI chatbots can make call centers 40% to 100% more productive and increase customer satisfaction. In healthcare, this means less wait time, fewer dropped calls, and better scheduling.

AI workflow helpers also offer:

• Automated appointment reminders, which lower no-show rates and improve money flow.
• Smart call routing, sending special questions to the right person or department.
• Data entry automation, reducing manual work for patient records or billing.
• Real-time dashboards for administrators to watch call volume, common problems, and AI performance.

Research shows AI automation delivers some of the best returns on investment among AI uses. For example, AI coding tools can boost developer speed by 10-30%, and AI chatbots save a lot of time on routine work. In a medical office, this means calls get handled faster and things run smoother, helping patients.

Challenges and Considerations for US Healthcare AI Adoption

While AI brings benefits, measuring its success has challenges for US healthcare groups:

• Data Quality: A KPMG survey found 85% of organizations say bad data is the biggest problem for AI success. In healthcare, missing or wrong patient info can hurt AI accuracy and affect phone responses or scheduling.
• Ethics and Compliance: Healthcare AI must follow HIPAA and ethical rules. Metrics that check fairness, detect bias, and keep transparency are needed so AI does not discriminate or break privacy laws.
• Governance and Auditing: AI governance systems help track AI use, log decisions, check performance, and keep things legal. Experts like Jacob Axelsen at Devoteam recommend these tools.
• Staff Adoption: The same KPMG report says only 24% of employees use AI regularly. Closing the gap between leaders and front-line staff is needed for AI to succeed.
• Continuous Monitoring: AI models can change over time, and their quality may drop as business changes. Automated dashboards and regular KPI checks catch problems early and keep AI aligned with goals.

Medical offices should start AI automation with small pilot projects focused on specific problems before expanding based on results.

Measuring ROI Beyond Traditional Financial Metrics

Traditional ROI looks only at money earned or saved but misses other AI benefits in healthcare. Data shows 65% of companies using generative AI report gains, but overall AI ROI can seem flat when only money is counted.

A wider ROI view includes:

• Lower costs by handling calls faster and with less staff.
• Better workforce productivity by moving staff to important tasks.
• Improved patient experience with better communication.
• Increased competitiveness and faster use of AI insights.
• Lower risks and stronger compliance with rules.

IDC reports generative AI brings 3.7 times the return for each dollar spent across industries, showing AI’s value beyond cost cutting. In medical offices, the return is even higher when benefits to patient care, staff morale, and accuracy are included.

Final Observations for US Healthcare Leaders

Using both direct and indirect KPIs to check AI performance gives a clear, complete picture of AI’s value in healthcare front-office work. US medical practice managers, owners, and IT staff need to understand both technical results and business effects to decide how to use AI tools like Simbo AI’s phone automation and answering services.

Tracking measures such as perplexity, mean squared error, first contact resolution, and patient satisfaction helps show how well AI works and affects patients. Adding ethics and governance checks makes sure AI follows laws and social standards. Also, focusing on staff acceptance and constant monitoring helps keep benefits lasting.

Since patient communication is very important, healthcare AI must be reliable, fair, and efficient. Combining technical scores with business outcomes helps US healthcare providers make smart choices, get the most from AI, and improve patient care.