📄 health informatics

Adaptation and Psychometric Validation of a Facility-Level Tool to Assess Telemedicine Readiness in Primary Care

This study successfully adapted and psychometrically validated the Telemedicine Readiness Inventory at the Facility Level (TRI-F) using data from 774 primary care facilities in Peru, demonstrating its structural validity, internal consistency, and utility for benchmarking and guiding telemedicine implementation planning.

Original authors: Escobar-Agreda, S., Villarreal-Zegarra, D., Reategui-Rivera, C. M., Paredes-Gonzales, Y., Rojas-Mezarina, L.

Published 2026-07-10

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Escobar-Agreda, S., Villarreal-Zegarra, D., Reategui-Rivera, C. M., Paredes-Gonzales, Y., Rojas-Mezarina, L.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine you're trying to build the ultimate treehouse. You have the blueprints, the wood, and even the nails. But before you start hammering, you need to know: Is your tree strong enough? Do you have the right tools? Is the neighborhood safe? And most importantly, is your team actually ready to climb up and live there?

That's exactly what this paper is about, but instead of treehouses, it's about telemedicine (doctors seeing patients through screens) in Peru's primary care clinics.

The Big Problem: The "Ready" vs. "Doing" Gap

During the pandemic, clinics everywhere suddenly started using video calls. It was a rush job. But once the emergency passed, many clinics stopped using them. Why? Because having a camera doesn't mean you're ready to use it every day.

The authors wanted to fix a broken measuring stick. There was an old tool (created by big health organizations) meant to check if a clinic was "ready" for telemedicine. But nobody had ever checked if that tool actually worked well. It was like using a ruler made of rubber to measure a race track—sure, it might stretch, but you can't trust the numbers.

The Mission: Sharpening the Ruler

The team took that old, rubbery ruler and sharpened it up. They called their new, improved version the TRI-F (Telemedicine Readiness Inventory at the Facility Level).

They tested this new ruler on 774 primary care clinics across Peru between December 2023 and March 2024. They asked clinic leaders to fill out a survey about five key areas:

Organizational Readiness: Is the boss on board?
Processes: Do we have a plan?
Digital Environment: Is the Wi-Fi and tech working?
Human Resources: Do we have enough trained staff?
Regulatory Issues: Are the rules clear?

(Note: They actually threw out one section called "Expertise" because it was too vague and didn't fit the math.)

The Results: Does the Ruler Hold Up?

Here is the cool part: The math says yes, the new ruler works!

It's Sturdy: When they broke the data down, the five sections they kept fit together perfectly, like pieces of a puzzle. The statistical "fit" scores were super high (ranging from 0.959 to 0.999 on a scale where higher is better).
It's Consistent: If you asked the same questions twice, you'd get the same answer. The internal consistency scores were solid, ranging from 0.75 to 0.87.
It's Fair: They checked if the tool worked the same way for big, complex clinics versus small, simple ones, and for people who had worked there for a long time versus those who just started. It did! The tool measures "readiness" equally for everyone, no matter who is holding the ruler.

The Reality Check: Ready Doesn't Mean "Doing"

Here is the twist. The authors compared the "readiness" scores to the actual number of video calls the clinics were making.

They found a positive link, but it was small. The correlation numbers were between 0.15 and 0.23.

Think of it like this: Having a fully stocked toolbox (high readiness) makes it easier to build a treehouse, but it doesn't guarantee you will actually build one. You still need to want to build it, have the time, and have someone to climb the ladder. The paper suggests that while being "ready" helps, it's not the only thing that decides if a clinic will use telemedicine. Other things—like patient demand, internet speed, or how much the staff likes using it—also play a huge role.

What This Means for the Future

The paper doesn't claim this tool is a magic wand that will solve all telemedicine problems. Instead, it's a diagnostic tool.

Imagine a doctor using a stethoscope. The stethoscope doesn't cure the patient; it tells the doctor what's wrong so they can fix it. This new tool is the stethoscope for clinics. It helps health officials look at a clinic and say, "Hey, your 'Digital Environment' score is low. Let's send you some new computers," or "Your 'Processes' score is weak. Let's train your staff on the workflow."

The authors emphasize that this tool is great for planning and prioritizing where to send help and money. But they are careful to say: Do not use this score to certify a clinic as "perfect" or "bad." A high score just means the clinic could do telemedicine well; it doesn't prove they are currently doing it perfectly.

The Bottom Line

The team successfully turned a shaky, untested measuring stick into a reliable, math-proven tool. They proved it works across different types of clinics in Peru. But they also reminded us that just because a clinic is ready (has the tools and plans), it doesn't automatically mean they are using telemedicine effectively. It's a necessary first step, but not the whole journey.

Technical Summary: Adaptation and Psychometric Validation of a Facility-Level Tool to Assess Telemedicine Readiness in Primary Care

Problem Statement
While telemedicine has expanded rapidly, particularly following the COVID-19 pandemic, its sustained integration into routine primary care remains uneven, especially in low- and middle-income countries (LMICs). A critical barrier to long-term continuity is the lack of reliable, psychometrically validated instruments to assess facility-level readiness. Existing tools, such as the Telemedicine Readiness Inventory developed by the Pan American Health Organization (PAHO) and the Inter-American Development Bank (IDB), have been utilized for descriptive assessments but lack rigorous quantitative evidence regarding their internal structure, reliability, and validity. Without psychometrically sound tools, health systems struggle to benchmark performance, prioritize investments, or monitor progress effectively. This study addresses the need to adapt and validate a facility-level instrument (TRI-F) specifically for primary care facilities (PCFs) in Peru to provide a robust metric for organizational readiness.

Methodology
The study employed a cross-sectional design following COSMIN standards for psychometric validation, utilizing secondary data from a national assessment conducted by the Peruvian Ministry of Health's Directorate of Telehealth.

Data Source: The dataset included responses from 774 PCFs across 25 regions of Peru, collected between December 2023 and March 2024. The sample was split randomly for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).
Instrument Adaptation: The original PAHO/IDB instrument (85 items, 6 domains) was adapted. The "Expertise" domain was removed as it reflected individual rather than institutional attributes. Item response options were refined from generic labels to specific Likert-level descriptions to reduce ambiguity. The final instrument comprised 53 items across five domains: Organizational Readiness, Processes, Digital Environment, Human Resources, and Regulatory Issues.
Analytical Approach:
- Structural Validity: Evaluated via EFA and CFA using polychoric correlation matrices and weighted least squares estimators. Model fit was assessed using CFI, TLI, RMSEA, and SRMR.
- Reliability: Internal consistency was measured using ordinal Cronbach's alpha and McDonald's omega.
- Measurement Invariance: Tested across facility complexity levels and respondent tenure using multigroup CFA, with $\Delta$ CFI < 0.010 as the criterion for invariance.
- Criterion Validity: Assessed by correlating domain scores with the proportion of telemedicine encounters relative to total outpatient encounters (derived from administrative Health Information System data).

Key Results

Structural Validity: The CFA demonstrated adequate model fit across all five domains. Fit indices ranged from CFI 0.959–0.999, TLI 0.952–0.997, RMSEA 0.028–0.065, and SRMR 0.016–0.057. Some items were removed during an iterative process to eliminate high correlated residuals.
Reliability: Internal consistency was acceptable to high for all domains, with Cronbach's alpha ( $\alpha$ ) ranging from 0.75 to 0.87 and McDonald's omega ( $\omega$ ) from 0.76 to 0.88.
Measurement Invariance: The instrument demonstrated scalar and metric invariance across facility complexity categories and respondent tenure (time working at the PCF), indicating that scores are comparable across these subgroups.
Criterion Validity: A positive but small correlation was observed between all five readiness domains and the proportion of telemedicine use ( $r_s = 0.15–0.23$ ; $p < 0.001$ ).
Normative Data: Percentile-based norms (1st to 99th) were established to facilitate local benchmarking, categorizing scores into low (1st–25th), medium (26th–74th), and high (75th–99th) readiness levels.

Significance and Claims
The authors claim that the adapted Telemedicine Readiness Inventory at the Facility Level (TRI-F) is the first instrument of its kind to undergo rigorous psychometric evaluation in a large-scale primary care setting. The study provides robust evidence of structural validity, reliability, and measurement invariance, supporting the tool's use for diagnostic and monitoring purposes.

The paper posits that while readiness is a necessary enabling condition for telemedicine implementation, it is not a sufficient determinant of service volume or quality, as evidenced by the modest correlations with actual telemedicine usage. Consequently, the authors argue that the TRI-F should be utilized primarily as a diagnostic tool to identify facility-level gaps, guide technical assistance, and prioritize resource allocation, rather than as a certification of high-quality delivery. The availability of validated norms allows health systems to benchmark facilities and monitor progress over time, addressing a critical gap in implementation science for digital health in LMICs. The study concludes that integrating such readiness assessments into broader implementation frameworks is essential for achieving sustainable and equitable telemedicine integration.