Operational and Financial Outcomes of Remote MRI & CT Scanning Models Across Rural and Urban Markets
- Jun 2
- 27 min read
Updated: Jun 3
Research conducted by Kyle Crowe, Tech Manager at RemoteRadTech, highlights how remote scanning helps both operational and financial performance for rural and urban outpatient imaging centers. Read the full report here.
Kyle Crowe
Department of Health Science, Northern Kentucky University
HCA 691: Healthcare Management Capstone
David B. Tataw, PhD, MPH, MPA, MMIS, FACHE
April 27, 2026
Email: crowek6@mymail.nku.edu
Abstract word count: 237
Main text word count: 5920
Table of Contents
Abstract
Rationale: Persistent shortages of MRI and CT technologists continue to disrupt access, reduce scanner utilization, and expose imaging organizations to operational and financial risk. Purpose: This capstone report examines remote MRI and CT scanning as a workforce strategy intended to extend technologist expertise across geographically distributed outpatient sites during vacancy periods. Methods: Using a retrospective outcomes-evaluation framework, the project compares vacancy-only periods with periods supported by RemoteRadTech remote coverage across rural and urban markets. The report integrates a current literature review, project rationale, operational definitions, and a structured methodology focused on utilization or uptime, completed exams per day, downtime minutes, cancellation rate, schedule fill rate, and a conservative revenue-opportunity proxy. Findings: The observed staffing dataset included 921 staffed segments, 646 staffed site-days, 7,237.75 remote coverage hours, and $274,759.60 in billed activity. Remote coverage increased from 1,210.75 hours in January to 1,705.00 hours in May. Site-informed modeling estimated 16,061 adjusted MRI exam opportunities across Sites A-C and E-J, while newly incorporated January-April Site D CT data identified 121 completed CT exams across 53 scan days, representing a $67,397 gross revenue-opportunity proxy. Conclusion: Remote MRI and CT scanning appears to be a practical workforce-continuity strategy, but its value should be evaluated through linked staffing, scheduling, RIS, and safety-governance data rather than billing activity alone.
Keywords: remote MRI scanning, remote CT scanning, radiology workforce shortages, scanner utilization, imaging operations, technologist staffing, healthcare access, outpatient imaging, operational outcomes, financial performance
Introduction and Literature Review
Healthcare organizations across the United States continue to experience persistent shortages of qualified MRI and CT technologists, and recent scholarship increasingly frames that problem as a structural operational challenge rather than a temporary disruption. In advanced imaging, the labor shortage affects more than staffing counts; it influences scanner uptime, throughput, scheduling stability, patient access, and the financial performance of high fixed-cost assets. For this capstone project, the central question is whether remote MRI and CT technologist coverage can improve scanner utilization, completed exam volume, and related operational and financial outcomes across rural and urban outpatient environments.
Current literature supports the relevance of this question because radiology workforce strain is now well documented. Siewert et al. (2025) identify inadequate labor force capacity as one of the major challenges facing radiology practice, while Christensen et al. (2025) and Rozenshtein et al. (2025) describe continued pressure on the future radiology workforce. Although some of this literature focuses on radiologists, the same operational logic extends to technologists, whose availability directly affects whether imaging services can actually be delivered. Mohammed et al. (2023) and Zarb et al. (2025) similarly describe recruitment, retention, and long-term workforce sustainability as persistent concerns within radiologic technology. When scanners cannot be staffed, organizations experience more than inconvenience; they lose productive capacity, defer diagnostic access, and underuse expensive equipment.
The literature also supports remote scanning as a credible emerging staffing model. Quinsten et al. (2023) explicitly present remote MR scanning as a potential solution to shortages of skilled radiographers. Hudson and Sahibbil (2022) describe remote scanning support in MRI as a meaningful change in radiographer practice, although they note professional tension and practical implementation challenges. More recent work adds to the feasibility argument. Konstantinidis et al. (2025) found that radiographers generally viewed remote MR scanning technology as useful and easy to use, while Walker et al. (2026) reported an initial clinical experience suggesting remote MRI support can expand access and staffing flexibility when implemented within a structured clinical environment.
Taken together, these studies suggest that remote imaging models are moving from conceptual feasibility toward practical implementation, but the strongest evidence remains concentrated in acceptance, workflow feasibility, and early clinical experience. Fewer studies directly connect remote technologist coverage with multisite throughput, scanner utilization, cancellation patterns, or financial opportunity. This distinction is important because the present capstone evaluates remote coverage as an operational model rather than only as a technology adoption issue.
A related body of evidence supports the broader virtualization of imaging work to improve efficiency. Li et al. (2022) found that non-patient-facing magnetic resonance radiology functions could be prioritized for virtual operations, improving efficiency and reducing waiting time. Bharadwaj et al. (2024) argue that radiology workflow tools should be selected according to measurable efficiency gains rather than novelty. Although these studies do not evaluate full remote scanner teleoperation across multiple client sites, they reinforce the logic behind this capstone: distributed expertise and remote workflow design may affect real operational outcomes and should be evaluated using explicit key performance indicators rather than assumptions.
The literature further supports the financial importance of studying remote staffing models. Workforce shortages create downstream costs through underused capital equipment, delayed scheduling, lost productivity, and inconsistent service delivery. Jhala et al. (2025) provides a relevant parallel in showing that a supplemental radiology staffing model can be evaluated through operational performance measures such as turnaround time. Nigatu et al. (2025) found that a teleradiology intervention improved patient waiting time and service satisfaction, suggesting that remote radiology-enabled models may reduce service delays where local capacity is constrained. While teleradiology is not the same as technologist teleoperation, both models depend on distributing specialized expertise across sites to maintain continuity.
At the same time, remote MRI and CT coverage cannot be judged on productivity alone. MRI is a high-risk environment, and the literature consistently emphasizes that any staffing innovation must preserve safety, role clarity, screening rigor, and emergency responsiveness. Pedrosa et al. (2025) summarize the 2024 update to the American College of Radiology Manual on MR Safety and identify remote operation as an area requiring careful governance. Kanal (2025) and Zandieh et al. (2025) likewise emphasize the practical importance of updated MR safety guidance. These sources are especially relevant to this capstone because they support inclusion of governance deliverables such as role delineation, escalation pathways, communication standards, and documentation controls.
The original project materials also provide useful implementation context. The American College of Radiology (2024) reinforces the importance of MR safety fundamentals in any operational model, while the Canadian Association of Radiologists (2021) provides guidance related to remote radiology practice. Alhasan and Alhasan (2025) discuss the technical requirements and optimization strategies needed for safe home-based teleradiology workstations. Although their article is not specific to remote scanner teleoperation, the infrastructure principles remain relevant because remote MRI and CT coverage also depends on secure systems, reliable connectivity, and clearly defined responsibilities.
Importantly, the literature is not uniformly confirmatory. A major challenge is that much of the remote scanning evidence remains descriptive, feasibility-oriented, or early-stage rather than robust multisite outcomes research. Hudson and Sahibbil (2022) show that remote support is not universally embraced, and Konstantinidis et al. (2025) found more modest enthusiasm regarding future use than current usefulness. In addition, workforce shortages are multifactorial. Mohammed et al. (2023) and Rozenshtein et al. (2025) suggest that staffing instability is shaped by broader forces involving recruitment, retention, burnout, service demand, and local workflow design. As a result, remote coverage may improve uptime and completed exams in some settings while yielding smaller, mixed, or negligible effects in others.
Another gap in the literature concerns financial outcomes tied specifically to remote MRI and CT teleoperation. Current studies support feasibility, acceptance, workflow potential, and safety requirements, but they do not yet provide a strong body of retrospective multisite evidence comparing vacancy-only periods with remote-coverage periods using standardized operational and financial indicators. That gap strengthens, rather than weakens, the rationale for this capstone. A retrospective evaluation with pre-specified definitions, transparent methods, and leadership-focused outputs is timely because it moves beyond conceptual support and asks whether remote coverage is associated with measurable improvement in utilization, throughput, downtime, and revenue opportunity proxies.
Overall, current literature supports three central conclusions. First, imaging workforce shortages are persistent, operationally disruptive, and financially consequential. Second, remote MRI and CT coverage is increasingly credible as a staffing strategy for extending expertise across geographically distributed sites. Third, implementation must be interpreted through the lens of governance and safety rather than throughput alone. The present project is therefore well aligned with current scholarship because it addresses an important evidence gap: whether remote technologist coverage produces measurable operational and financial benefits across diverse rural and urban outpatient settings.
Project Rationale, Purpose, Objectives, and Setting Background
The rationale for this project is grounded in the intersection of workforce shortage, imaging access, and stewardship of high fixed-cost assets. MRI and CT scanners represent major capital investments whose value depends on reliable operational coverage. When technologist vacancies leave scanners idle, organizations lose available capacity, patients experience delayed diagnostic pathways, and leadership faces both operational and financial pressure. Remote technologist coverage has emerged as a plausible response because it may allow specialized expertise to be extended across multiple sites without requiring every location to fill vacancies immediately with onsite staff.
The specific problem addressed in this capstone is whether remote MRI and CT coverage can reduce the operational harms associated with vacancy-only periods. Those harms include lower utilization, fewer completed exams per day, increased downtime, higher cancellation pressure, and reduced schedule stability. The project is important to healthcare administration because it evaluates remote scanning not as an abstract technology, but as a workforce and continuity strategy that could inform staffing, budgeting, service planning, and risk management decisions.
The purpose of the study is to conduct a retrospective outcomes evaluation comparing vacancy-only periods with periods supported by contracted RemoteRadTech remote coverage across a selection of outpatient MRI and CT sites in rural and urban markets. The principal question is whether remote coverage is associated with better operational performance, measured primarily by scanner utilization or uptime and completed exams per day. Secondary indicators include downtime minutes, cancellation rate, schedule fill rate, and a conservative financial proxy based on completed exams and average net revenue assumptions.
The setting background for this project is a multi-site outpatient imaging environment in which remote coverage is used to support client organizations experiencing staffing shortages, extended-hour needs, or coverage-restoration gaps. The January-May 2026 staffing and billing dataset included 10 anonymized outpatient sites, 646 staffed site-days, 921 staffed segments, 7,237.75 remote coverage hours, and $274,759.60 in billed coverage activity. The portfolio included MRI extension sites and one CT coverage-restoration site. In practical terms, this makes the project suitable for a site-day level evaluation, because the comparison of vacancy-only and remote-coverage periods can be organized around real operational schedules while still protecting site identity and patient confidentiality.
Four learning objectives guided the project. First, the capstone was designed to strengthen analytical thinking by converting a familiar imaging operations issue into a structured healthcare administration evaluation. Second, it was intended to strengthen strategic orientation by framing remote scanning as a workforce strategy and leadership decision problem rather than only a technical solution. Third, it aimed to build project and process management skills through pre-specified definitions, denominator rules, subgroup logic, and bias-mitigation steps. Fourth, it sought to improve communication and collaboration by translating modality-specific operational concerns into the language of healthcare administration, quality oversight, and organizational performance.
These objectives align with the NKU capstone expectation that students integrate analytical thinking, strategic orientation, collaboration, communication, project management, process management, and self-development within an applied project. In this report, those competencies are reflected in the operational definitions, the design of a pre-specified methodology, the attention to governance and safety, and the leadership-focused framing of expected outputs. The project therefore serves both an organizational purpose and an educational one: it addresses a real imaging services problem while demonstrating graduate-level competency in evidence-based healthcare management.
Table 1Primary and Secondary Outcome Measures
Measure | Type | Operational definition | Decision relevance |
Utilization/uptime | Primary | (Uptime minutes ÷ scheduled scanner minutes) × 100; uptime = scheduled minutes minus downtime minutes. | Core indicator of restored productive scanner capacity |
Completed exams/day | Primary | Count of completed exams with valid completion timestamps; multi-sequence protocols counted as one exam. | Direct throughput and access measure |
Downtime minutes | Secondary | Minutes lost during scheduled operating time, coded by cause. | Distinguishes staffing effects from technical or workflow losses |
Cancellation rate | Secondary | Cancelled appointments ÷ scheduled appointments using a fixed denominator cut-off. | Reflects schedule instability and access friction |
Schedule fill rate | Secondary | Filled slots ÷ available slots. | Assesses extent to which restored coverage is translated into scheduled use |
Revenue opportunity proxy | Secondary | Completed exams/day × average net revenue assumption. | Provides conservative leadership-facing financial context |
Methods and Project Description/Processes
This study uses a retrospective, observational outcomes-evaluation design comparing vacancy-only periods with periods supported by contracted RemoteRadTech coverage across outpatient MRI and CT operations. The design is explicitly decision oriented. Rather than testing a clinical intervention, it evaluates whether a staffing model change is associated with measurable differences in operational utilization, throughput, and related access and financial indicators. Because the comparison is nonrandomized, the methodology emphasizes pre-specification of outcomes, denominator rules, inclusion and exclusion criteria, and subgroup analyses to reduce analytic flexibility and interpretive bias.
The general overview of the internship setting is a real-world imaging operations environment in which remote MRI and CT staffing support is provided to multiple client organizations. Based on the uploaded monthly operational files, the project draws from a multi-site roster that includes eleven site-level workbooks representing different markets and, in at least one case, separate MRI and CT modality tabs. The internship activities centered on defining the problem, building the evaluation model, aligning the study with current literature, establishing operational definitions, and preparing the dataset and governance framework for later analysis.
The unit of analysis is the site-day. This unit is appropriate because staffing coverage, downtime, completed exams, and schedule availability are operationally experienced at the scanner-day level even when organizational decisions occur at higher levels. Comparing outcomes by site-day allows the study to distinguish between periods with no remote support and periods with active remote coverage while still accounting for clustering within sites.
Sample selection follows the principle of including outpatient MRI and CT operations for which sufficiently consistent operational records are available and where vacancy-only and remote-coverage periods can be identified. Inclusion criteria are site-days occurring during the defined evaluation windows in which the scanner was scheduled to operate and where required data elements are available for the selected outcomes. Exclusion criteria include non-operating days, planned closures and holidays handled consistently across sites, and full-day service outages when those days are excluded from primary analysis and instead evaluated through sensitivity checks. Partial shifts are retained using the day-specific scheduled minutes as the denominator.
Two primary outcomes are specified. The first is scanner utilization or uptime, calculated as uptime minutes divided by scheduled scanner minutes multiplied by one hundred. Uptime minutes are defined as scheduled minutes minus documented downtime minutes. The second is completed exams per day, defined as the number of exams with completed status and valid completion timestamps during the operating day. Multi-sequence protocols are counted as one exam so volume is not artificially inflated. Three secondary outcomes are also specified: downtime minutes during scheduled operating time; cancellation rate using a fixed denominator cut-off to handle add-ons consistently; and schedule fill rate defined as filled slots divided by available slots. A conservative revenue-opportunity proxy is calculated as completed exams per day multiplied by average net revenue assumptions.
Data collection is organized around a fixed data dictionary. First, comparison periods are identified so that vacancy-only and remote-coverage windows are defined before outcome extraction begins. Second, site-level operational data are extracted using the same KPI rules across sites. Third, consistency rules are applied at the point of collection, including the treatment of partial shifts, closure days, add-ons, and missing downtime coding. Fourth, governance and safety linkage is preserved by documenting the presence of role delineation, escalation pathways, and communication standards where available. This process is intended to keep the operational analysis anchored to the conditions under which remote coverage is actually delivered.
Data storage and analysis are planned within secure organizational processes appropriate to a retrospective operations evaluation. The project materials indicate that data cleaning and documentation are expected to occur in Excel with a fixed data dictionary. Because site-days are nested within sites, the study plans to account for clustering using site fixed effects or mixed-effects models with random intercepts. McNeish and Kelley (2019) support this attention to clustered data structures, and the STROBE framework remains relevant for transparent reporting of observational studies (von Elm et al., 2007).
Analytically, the project begins with descriptive summaries for each KPI by site, modality when feasible, and comparison period. Primary comparisons evaluate whether utilization and completed exams per day differ meaningfully between vacancy-only and remote-coverage periods. Secondary analyses assess downtime minutes, cancellations, and fill rate. Subgroup analyses compare rural and urban sites and stratify MRI versus CT when data support that distinction. Sensitivity analyses test the robustness of findings when closure days or service-outage days are handled differently. Throughout the analysis, comparison periods are labeled generically during the statistical phase to reduce interpretive bias.
Criteria for success are defined in both practical and statistical terms. Primary success would be a meaningful improvement in utilization or uptime and completed exams per day during remote-coverage periods, reported with confidence intervals. Secondary success would include reduced downtime and improved schedule stability. Governance success requires that operational gains not be interpreted apart from safety expectations. In other words, a favorable staffing effect is only meaningful if the remote model is implemented with adequate role clarity, escalation pathways, and communication controls.
A key methodological boundary is that the available staffing workbooks did not contain downloadable scanner schedules, RIS exam-completion extracts, or patient-level cancellation files. Therefore, the observed analysis is primarily limited to staffing-and-billing activity, with the exception of newly supplied January-April Site D daily CT totals. The KPI section continues to use site-informed operational assumptions for MRI capacity estimates while now incorporating observed CT volume for Site D. This approach improves specificity over a generic model, but it does not convert the MRI KPI estimates into audited scanner-log findings. The methods section is therefore intentionally transparent: it establishes an evaluation framework capable of supporting decision-grade evidence while clearly distinguishing observed data from modeled operational estimates.
Figure 1. Conceptual framework linking workforce shortage, remote coverage, operational outcomes, and governance requirements.
Results and Project Outcomes
This section reports empirical findings from the uploaded monthly staffing workbooks rather than only anticipated outcomes. After cleaning 10 source files covering January through May 2026, the analytic dataset contained 921 staffed segments across 10 anonymized sites. Those segments represented 7,237.75 staffed remote-coverage hours and $274,759.60 in billed activity. The weighted average hourly rate across all records was approximately $38.00 per hour. These findings are important because they confirm that remote coverage was not sporadic or anecdotal during the study window; it was deployed at measurable scale across multiple outpatient environments.
The empirical analysis also clarifies the scope of what the available data can support. The monthly workbooks consistently captured staffing date, hours, hourly rate, and billed total, which makes them appropriate for describing remote coverage volume and associated billed activity. Newly supplied daily CT totals also allow Site D to be summarized by observed completed CT volume from January through April. However, the files still do not contain downloadable MRI completed-exam counts, scheduled scanner minutes, cancellations, downtime taxonomies, or schedule fill-rate fields. As a result, this capstone can now report real staffing-and-billing outcomes and observed Site D CT volume, while the originally planned full KPI set still requires supplemental RIS, scheduling, or scanner-source data before MRI utilization, throughput, and schedule-stability comparisons can be completed.
A clear month-over-month growth pattern was observed. Total staffed hours increased from 1,210.75 in January to 1,287.50 in February, 1,366.75 in March, 1,667.75 in April, and 1,705.00 in May. Billed activity followed a similar trajectory, rising from $43,490.75 in January to $45,872.00 in February, $50,116.60 in March, $69,066.75 in April, and $66,213.50 in May. April showed the sharpest increase in both hours and billed activity, suggesting either expanding client adoption, higher coverage demand, longer staffed shifts, or a combination of those factors.
Coverage was also concentrated across a relatively small number of sites. Site A accounted for 3,643.00 staffed hours and $83,789.00 in billed activity, or just over half of all recorded hours. Site B contributed 1,055.50 hours and $57,206.00, while the remaining sites were materially smaller in volume. This concentration pattern suggests that remote scanning may function differently across markets: for some sites it appears to serve as a high-intensity continuity model, whereas for others it operates more as intermittent supplemental coverage.
The weekday-weekend comparison adds further operational context. Weekday activity accounted for 6,017.50 hours and $219,050.85 in billed coverage, while weekend activity accounted for 1,220.25 hours and $55,708.75. Weekend work therefore represented a smaller share of total volume but carried a higher weighted average rate than weekday work. Operationally, that pattern implies that weekend coverage may be used more selectively, potentially for premium coverage windows, harder-to-staff periods, or continuity support where local onsite coverage is less stable.
At the same time, these findings should be interpreted as a staffing-and-billing analysis with a limited observed CT-volume supplement rather than a complete scanner-performance analysis. The current results do not yet determine whether the additional remote MRI coverage translated into more completed exams, less downtime, fewer cancellations, or higher scanner utilization relative to vacancy-only periods. Instead, they establish that remote coverage was deployed at meaningful scale, that deployment expanded during the observation window, that Site D produced measurable CT volume during January-April, and that the financial footprint of the model can be quantified from available operational files.
Taken together, the present empirical findings already strengthen the capstone in an important way. The project is no longer limited to a conceptual or purely anticipated evaluation; it now documents a measurable remote-coverage operating pattern across multiple anonymized sites and observed CT exam volume for Site D. The next analytic step is to link these staffing patterns to scanner-level operational outcomes using supplemental source systems so that the broader question of utilization, throughput, and access impact can be answered with the same degree of transparency.
Table 2
January-May 2026 Remote Coverage Summary by Month
Month | Staffed segments | Total hours | Total billed | Weighted avg. rate |
January 2026 | 155 | 1,210.75 | $43,490.75 | $36.60 |
February 2026 | 162 | 1,287.50 | $45,872.00 | $35.63 |
March 2026 | 170 | 1,366.75 | $50,116.60 | $36.64 |
April 2026 | 206 | 1,667.75 | $69,066.75 | $41.41 |
May 2026 | 228 | 1,705.00 | $66,213.50 | $42.07 |
Table 3KPI Alignment for Current Dataset and Site-Informed Estimates
KPI or data element | Status | Current treatment | Interpretive boundary |
Remote coverage hours / billed activity | Observed | Calculated directly from staffing workbooks | Supports staffing scale and financial footprint |
Staffed site-days | Observed | Calculated from staffing records by site and date | Supports site-day modeling but not patient-level outcomes |
Completed exam capacity | Site-informed modeled / observed CT supplement | MRI capacity estimated from confirmed slot lengths and staffed hours; Site D CT summarized from Jan-Apr daily totals | MRI estimates do not equal audited completed-exam counts; CT totals are limited to Jan-Apr |
Delay and cancellation effects | Site-informed estimate | Incorporates reported delay and no-show patterns where quantifiable | Protected schedules could not be downloaded |
Utilization, fill rate, and revenue proxy | Modeled / observed CT revenue supplement | MRI derived from capacity, delay, and cancellation assumptions; Site D CT revenue proxy derived from 121 observed CT exams × $557 | Requires RIS/scheduler linkage for final validation |
Site-Informed KPI Section (Clearly Labeled Modeled Estimate)
Operational clarification supplied after the initial analysis showed that Site D functioned differently from the remaining sites. Site D represented CT coverage restoration, meaning RemoteRadTech coverage created scanning availability that otherwise depended on order-driven emergency department and outpatient demand. Newly supplied January-April CT data now allow Site D to be summarized using observed daily CT totals rather than staffing hours alone. Sites A-C and E-J primarily represented MRI coverage extension or as-needed support. For that reason, Site D remains separate from the MRI slot-based model, but it is now included as an observed CT-volume supplement.
The revised KPI model uses definitive site-level operational information from protected schedule review. Site A and Site J have variable 15- to 45-minute appointment blocks with an average of 20 minutes. Sites B, E, H, and I use 30-minute time slots. Sites C, F, and G use 45-minute time slots. Site D does not use a fixed slot template, so it remains inappropriate to estimate CT capacity from staffing hours alone; however, the January-April daily totals now provide observed completed CT exam counts for that site. The model also incorporates site-reported delay and cancellation/no-show patterns where those patterns were specific enough to estimate.
Because protected MRI schedules could not be downloaded due to HIPAA requirements, the MRI values remain site-informed planning estimates rather than audited scanner-log outcomes. This is a stronger model than the earlier uniform 30-minute, no-delay assumption, but it should still be interpreted as estimated MRI capacity and expected completed-exam opportunity rather than confirmed MRI completed exams. By contrast, the newly supplied Site D CT figures are observed daily totals for January-April. The combined model is useful for leadership planning because it translates documented remote coverage hours into practical capacity ranges while preserving the distinction between observed CT volume, observed staffing/billing activity, and modeled MRI opportunity.
Table 4Site-Informed Scheduling, Delay, and Cancellation Assumptions
Site | Slot assumption | Delay pattern | Cancellation/no-show pattern |
A | Variable 15-45 min; 20-min average | Average 30-min delay two days/week | Average 2-3 cancellations/day |
B | 30-min slots | No delays reported | Rare cancellations/no-shows |
C | 45-min slots | No consistent delays reported | 1-2 times/week with 2-3 cancellations/no-shows |
D | No fixed slots; order-driven CT coverage | No delays due to provided coverage | No cancellations due to coverage-restoration model |
E | 30-min slots | Rare delays; maximum reported 30 min | Usually 1-2 cancellations/no-shows/day |
F | 45-min slots | No delays on RemoteRadTech scan days | No cancellations/no-shows on RemoteRadTech scan days |
G | 45-min slots | No delays on RemoteRadTech scan days | No cancellations/no-shows on RemoteRadTech scan days |
H | 30-min slots | Rare delays; maximum reported 30 min | 1-2 cancellations/no-shows/week |
I | 30-min slots | No delays reported | 1-2 cancellations/no-shows/week |
J | Variable 15-45 min; 20-min average | No delays reported | No cancellations/no-shows reported |
Table 5Revised Site-Informed Modeled KPI Estimates
Site | Staffed days | Hours | Slot model | Adjusted MRI exam opportunity | Gross revenue proxy |
A | 122 | 3,643.00 | 20-min avg.; delay/cancel adjusted | 10,559 | $13,991,054 |
B | 143 | 1,055.50 | 30-min; no adjustment | 2,111 | $2,797,075 |
C | 41 | 418.75 | 45-min; no-show adjusted | 528 | $699,048 |
D | 85 | 425.25 | Observed Jan-Apr CT volume; no fixed slots | 121 observed CT exams | $67,397 |
E | 55 | 506.00 | 30-min; cancellation adjusted | 930 | $1,231,588 |
F | 34 | 340.00 | 45-min; no adjustment | 453 | $600,667 |
G | 40 | 340.00 | 45-min; no adjustment | 453 | $600,667 |
H | 89 | 226.25 | 30-min; weekly no-show adjusted | 426 | $564,185 |
I | 32 | 238.00 | 30-min; weekly no-show adjusted | 466 | $617,980 |
J | 5 | 45.00 | 20-min avg.; no adjustment | 135 | $178,875 |
MRI total, excluding Site D | 561 | 6,812.50 | Site-specific slot model | 16,061 | $21,281,137 |
Using these revised site-informed assumptions, the MRI extension and as-needed sites were associated with approximately 16,061 adjusted MRI exam opportunities across 561 staffed site-days. This estimate is lower than a uniform no-delay/no-cancellation model for some sites because it incorporates reported delay and cancellation patterns, especially at Sites A and E. Site D remains the only CT coverage-restoration site and is intentionally excluded from the slot-based MRI completed-exam estimate because its volume depends on emergency department and outpatient orders rather than a fixed appointment template. For Site D, the newly supplied January-April daily totals identify 121 completed CT exams across 53 scan days, equal to an average of 2.28 CT exams per scan day and an estimated $67,397 gross revenue-opportunity proxy using the $557 average CT assumption.
Table 6Observed Site D CT Volume by Month, January-April 2026
Month | CT scan days | Completed CT exams | Avg. CT exams/scan day | Gross revenue proxy |
January 2026 | 10 | 21 | 2.10 | $11,697 |
February 2026 | 15 | 33 | 2.20 | $18,381 |
March 2026 | 18 | 34 | 1.89 | $18,938 |
April 2026 | 10 | 33 | 3.30 | $18,381 |
January-April total | 53 | 121 | 2.28 | $67,397 |
Discussion, Limitations, and Recommendations
The discussion section now benefits from observed staffing-and-billing evidence, observed Site D CT volume, and a more defensible site-informed KPI model. The observed January-May 2026 workbook analysis demonstrates that remote coverage was deployed at meaningful scale across multiple outpatient sites and generated measurable staffing and billing activity. The revised KPI section extends that evidence using confirmed site scheduling patterns, reported delay patterns, cancellation/no-show estimates, and the newly incorporated January-April CT totals. Most importantly, the project now shows that remote coverage served at least two operational functions: MRI capacity extension or as-needed support at most sites and CT service restoration at Site D.
A second implication is that remote coverage is not distributed evenly. Activity was concentrated heavily in Site A and Site B, while several other sites relied on remote staffing much more selectively. This uneven distribution suggests that healthcare leaders should avoid assuming a single use case. In some markets remote coverage may function as a primary continuity mechanism; in others it may act as occasional support, overflow relief, or premium coverage for harder-to-staff windows.
The project also highlights several limitations. First, the retrospective and nonrandomized design remains vulnerable to confounding from seasonality, protocol mix, local staffing changes, referral patterns, scanner service events, and differences in hours of operation. Second, the available operational files quantify staffing hours, rates, billed totals, and staffed days but do not include downloadable scanner-level fields needed to confirm MRI completed exams, uptime denominators, cancellation rates, downtime classification, or schedule fill rate. Third, although January-April Site D CT totals are now observed, they do not include May CT volume or patient-level order timing, acuity, cancellation, or downtime detail. Fourth, although the revised KPI model incorporates site-specific scheduling knowledge, the underlying MRI schedules could not be downloaded because of HIPAA-related confidentiality constraints. Fifth, safety events are rare, which means the study is unlikely to be powered to detect small differences in event rates. Finally, because remote coverage can serve different functions across settings, pooled results may conceal important local variation.
These limitations do not invalidate the capstone, but they do define the boundary of what can be claimed from the present data. The strongest current conclusion is that remote coverage was deployed at meaningful scale, that its billed activity can be quantified, that site-specific scheduling details make the modeled MRI KPI estimates more realistic than the earlier uniform assumption, and that Site D generated 121 observed CT exams from January through April. However, the stronger causal question-whether remote coverage improved productivity relative to vacancy-only periods-still requires linkage to RIS, scanner operations, scheduling, and exam-completion data.
Several recommendations follow from the work completed. First, the next empirical phase should preserve the pre-specified analysis plan and link the staffing dataset to RIS, scanner, or scheduling sources using standardized site-day identifiers. Second, denominator fields for uptime, cancellations, and fill rate should be audited early so those KPIs are computed consistently across sites. Third, the Site D CT daily-volume approach should be extended through May and paired with order timestamps so CT access and throughput can be evaluated more fully. Fourth, rural-versus-urban and MRI-versus-CT subgroup logic should be retained because the staffing-intensity pattern already suggests heterogeneity in how sites use remote coverage. Fifth, governance documentation should continue to accompany the operational results so that implementation quality is interpreted alongside performance. Sixth, implementation planning should account for platform differences, onsite assistant readiness, real-time communication reliability, escalation pathways, and local scheduling variation because these practical factors influence whether remote coverage can translate into completed exams.
A final recommendation concerns leadership use of the results. Even before full KPI linkage is complete, these findings can inform workforce planning by showing where remote coverage demand is concentrated, where weekend staffing carries premium cost, and where billing activity is growing most quickly. If subsequent scanner-level analyses are favorable, organizations should scale remote coverage with explicit role delineation, real-time communication standards, escalation rules, and routine review of both operational and safety indicators.
Conclusions
This capstone report addresses a timely healthcare administration question: whether remote MRI and CT technologist coverage can improve operational and financial performance during staffing shortages across rural and urban outpatient markets. The literature indicates that workforce shortages in radiology are persistent, that remote scanning is increasingly credible as a staffing response, and that governance and safety remain nonnegotiable. Building on that evidence, this report includes an empirical analysis of January-May 2026 staffing workbooks, newly supplied January-April Site D CT totals, and a revised site-informed KPI model. The observed staffing analysis identified 921 staffed segments across 10 anonymized sites, totaling 646 staffed site-days, 7,237.75 remote coverage hours, and $274,759.60 in billed activity, with a visible upward trend in coverage volume from January through May. The revised KPI model estimates approximately 16,061 adjusted MRI exam opportunities across the MRI extension and as-needed sites, while Site D contributed 121 observed CT exams across 53 January-April scan days.
These findings do not yet represent audited scanner-level outcomes, but they provide a stronger operational picture than staffing hours alone. Noticeably, the project documents that remote coverage was deployed at measurable scale and can be translated into practical capacity estimates. Educationally, it demonstrates analytical thinking, strategic orientation, process discipline, and leadership framing applied to a real imaging-services problem. The next phase should link staffing activity to scheduler, RIS, and scanner-source data so that leaders can judge remote coverage by access, throughput, continuity, cost, and safe implementation.
Reflections
My internship and capstone experience centered on the development of this project, and the most significant work completed so far has included identifying the healthcare administration problem, defining the purpose of the study, establishing key performance indicators, completing the literature review, and building the methodological framework for the retrospective evaluation. Although the data analysis has not yet been fully completed, the project has advanced substantially through the design phase. That progress has shown me that a meaningful healthcare administration project depends not only on having an important problem, but on defining that problem in a way that can support transparent and useful evaluation.
One of the strongest lessons I learned is that healthcare operational problems are rarely caused by one issue alone. In this project, workforce shortages, imaging access, workflow design, organizational readiness, governance, and financial performance are all interconnected. That realization strengthened my analytical thinking because it pushed me beyond practical familiarity with imaging operations and required me to operationalize variables, define denominator rules, and think carefully about how leaders would interpret results. The process also reinforced the importance of pre-specified outcomes and analytic discipline so that conclusions remain credible rather than anecdotal.
The project also strengthened my strategic orientation. Rather than thinking about remote scanning only as a technical innovation, I learned to frame it as a workforce strategy and continuity model that affects access, capacity, and stewardship of high fixed-cost assets. That shift in perspective matters to my career development because it reflects movement from modality-specific expertise toward systems-level healthcare administration thinking. I increasingly understand staffing problems not merely as scheduling issues, but as strategic organizational problems involving finance, service continuity, policy, quality oversight, and leadership decision-making.
Project management and process management were also central to my learning. Because the capstone is a retrospective outcomes evaluation, success depended on developing a structured process before interpretation could occur. This included identifying comparison periods, defining inclusion and exclusion criteria, creating a fixed data dictionary, planning subgroup and sensitivity analyses, and clarifying how governance information would be linked to operational findings. Working through these steps improved my ability to build a multi-phase project systematically rather than trying to move directly from problem recognition to conclusions.
Another important area of growth involved communication and collaboration. My professional background is rooted in imaging practice and MRI safety, but this capstone required me to explain imaging operations issues in the language of healthcare administration. That meant translating throughput disruption, underused scanner time, staffing gaps, and remote support workflows into concepts relevant to leadership audiences. It also required responsiveness to faculty expectations, capstone requirements, and scholarly writing standards. I now feel more prepared to communicate specialized operational problems in a way that broader administrative stakeholders can understand and use.
The institutional environment both helped and challenged the project. On the positive side, the organizational relevance of staffing shortages made the topic practical and worth pursuing. At the same time, healthcare organizations operate within real constraints such as competing priorities, limited time, variable data quality, and complex decision pathways. Personally, the biggest challenges I faced were balancing graduate work with full-time professional responsibilities and continuing to develop the project while final analysis was still pending. I addressed those challenges by approaching the capstone in phases and focusing first on design strength, literature alignment, and methodological clarity.
Overall, I believe the learning objectives tied to analytical thinking, strategic orientation, communication, collaboration, project management, process management, and self-development were accomplished to a substantial degree, even though the final empirical phase remains in progress. Most importantly, the capstone sharpened my identity as both an imaging professional and an emerging healthcare administrator. It reinforced that innovation in healthcare operations must be judged not only by feasibility, but by evidence, safety, strategic value, and sustainability. If I were revising the experience, I would seek earlier data-access checkpoints and a more structured transition from design into analytic execution. Even so, this project has been a valuable bridge between academic preparation and applied healthcare leadership.
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Appendix A
Anonymized Site-Level Staffing and Billing Summary
Anonymized site | Staffed segments | Total hours | Total billed | Share of total hours |
Site A | 356 | 3,643.00 | $83,789.00 | 50.3% |
Site B | 163 | 1,055.50 | $57,206.00 | 14.6% |
Site C | 41 | 418.75 | $23,031.25 | 5.8% |
Site D | 85 | 425.25 | $23,013.25 | 5.9% |
Site E | 55 | 506.00 | $21,296.10 | 7.0% |
Site F | 34 | 340.00 | $20,060.00 | 4.7% |
Site G | 40 | 340.00 | $19,720.00 | 4.7% |
Site H | 110 | 226.25 | $13,909.00 | 3.1% |
Site I | 32 | 238.00 | $10,710.00 | 3.3% |
Site J | 5 | 45.00 | $2,025.00 | 0.6% |
Appendix B
Modeled KPI Assumptions and Interpretation Notes
The site-informed KPI section was built from observed staffed-hour and staffed-day files, supplemented by protected schedule review that could not be downloaded because of HIPAA requirements and newly supplied January-April Site D CT daily totals. Revised assumptions were supplied by site: Site A and Site J used variable 15- to 45-minute appointment slots with a 20-minute average; Sites B, E, H, and I used 30-minute slots; Sites C, F, and G used 45-minute slots; and Site D had no fixed slot template because CT coverage depended on emergency department and outpatient order volume. Delay and cancellation/no-show assumptions were incorporated only when the site information was specific enough to estimate. The MRI values remain planning estimates rather than observed site performance because the workbooks do not include scanner audit logs, actual MRI completed-exam extracts, or downloadable scheduler denominators. Site D CT volume is the exception: January-April daily totals identified 121 completed CT exams across 53 scan days, with a gross revenue-opportunity proxy of $67,397 using the $557 average CT assumption. The March source entry listed as “0-1” was treated as March 20 = 1 CT based on its position in the date sequence.
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