The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
Hospitals do not get to “pause” when infrastructure fails. A momentary power interruption, medical gas pressure drop, domestic water issue, boiler outage, or chilled-water disruption can quickly move from inconvenience to clinical risk. That is why utility asset risk assessment in healthcare must be more rigorous than a generic maintenance ranking exercise. It needs to connect the clinical consequences of failure, the likelihood that failure could occur, and the organizational controls in place to detect, respond, and recover. That logic is consistent with Suzane Greeman’s Risk-Based Asset Criticality Assessment Handbook, which emphasizes aligning asset criticality with both business drivers and asset drivers so organizations can convert failure impacts and probability factors into actionable risk information. It is also consistent with NFPA 99’s risk-based categorization of healthcare systems and DNV’s NIAHO® model, which expects hospitals to manage the physical environment through documented processes, annual evaluation, measurement, trend analysis, and continuous improvement.
In practice, many hospitals still use simplistic labels such as “critical,” “important,” or “noncritical” without documenting the rationale behind those assignments. That is rarely enough in a hospital setting. Greeman’s approach is valuable because it moves the discussion beyond opinion and asks leaders to define what matters most to the organization, then translate those priorities into consequence and probability indicators. NFPA 99 strengthens this by requiring healthcare systems to be categorized according to the risk of injury to patients, staff, or visitors if a system fails. DNV then adds an operational governance layer: hospitals must maintain safe facilities, establish policies, and procedures for managing impairments and unfavorable occurrences, evaluate physical environment systems at least annually, and analyze incidents for trends that require improvement. In other words, hospitals need a risk model that is not just technically sound, but also survey-ready, operationally defensible, and useful to leadership.
A hospital’s utility infrastructure is directly tied to patient care. Unlike many commercial environments, utility failure here can affect surgery, ventilator-dependent patients, sterile processing, imaging uptime, infection control, medication storage, and emergency operations. NFPA 99 explicitly establishes criteria for levels of healthcare services and systems based on the risk those failures pose to patients, staff, and visitors, with the goal of minimizing fire, explosion, and electrical hazards. The risk categories in NFPA 99 are familiar but essential: Category 1 systems are those whose failure is likely to cause major injury or death; Category 2 is likely to cause minor injury; Category 3 is not likely to cause injury but could cause discomfort; and Category 4 has no impact on patient care. This categorization provides a baseline clinical lens for utility systems, but it should not be mistaken for a complete asset prioritization methodology.
That is where a risk-based asset criticality methodology becomes useful. Greeman’s framework notes that consequence of failure alone provides only a partial picture; a stronger method combines consequence with probability of failure to create a more complete view of exposure. For hospitals, that means a Category 1 electrical component with strong redundancy, low failure history, tight PM discipline, online monitoring, and fast response capability may still require strict control, but its total risk profile may differ from another Category 1 component with aging condition, deferred maintenance, recurring alarms, and no practical back-up. In short, clinical impact defines the floor of seriousness; reliability and resilience factors determine how urgently the organization should intervene.
Greeman’s method begins by identifying business drivers and converting them into consequences of failure. In a hospital, those drivers should reflect both patient care and enterprise performance. Typical business drivers include: patient safety, continuity of clinical operations, regulatory compliance, infection prevention, emergency preparedness, financial performance, reputation, and staff safety. A utility asset assessment becomes much more credible when it explicitly asks, “If this asset fails, how would it affect each of these drivers?” rather than relying on a generic score from engineering alone.
For example, a medical air compressor does not merely support “operations.” Its failure may affect respiratory therapy, surgical support, alarm burden, patient safety, and escalation workflows. A domestic hot-water return pump might have lower direct clinical severity than a life safety branch transfer switch, but in some settings it could still affect environmental hygiene, handwashing capability, and patient experience. A chiller plant may not always present immediate life-threatening risk in the same way as essential electrical distribution, yet sustained failure on a summer day in a southern climate can cascade into OR schedule disruptions, MRI downtime, pressure-relationship challenges, and deployment of portable cooling measures. The point is that utility risk scoring should reflect the actual care environment and operating context, not just the equipment class.
The most practical way to structure hospital utility consequence scoring is to anchor it in NFPA 99 categories. Hospitals should first map systems and subsystems to the applicable clinical risk category, because NFPA 99 is the accepted language for determining how interruption or failure impacts patients and caregivers. This helps standardize discussions between facilities, safety, clinical engineering, nursing leadership, infection prevention, and accreditation teams. A failure mode that could lead to major injury or death should never be flattened into the same scoring scale as a comfort-only disruption without explicit differentiation.
However, NFPA 99 categories should be the beginning—not the end—of the assessment. The category tells you the severity band of failure consequences, but hospitals still need to determine which assets within that band deserve the most immediate capital, maintenance, monitoring, or contingency attention. One common best practice is to translate NFPA 99 into a consequence score such as: Category 1 = 5, Category 2 = 4, Category 3 = 2, Category 4 = 1, then adjust or supplement that with facility-specific consequence dimensions such as mission interruption, compliance exposure, and recovery complexity. That preserves code-based seriousness while still producing a practical prioritization model.
Greeman’s framework is especially useful here because it does not stop at consequence. It asks organizations to define asset drivers and convert them into probabilities of failure. In a hospital utility environment, those drivers usually include age and lifecycle position, condition assessment findings, maintenance history, test performance, alarm history, environment of use, manufacturer supportability, known obsolescence, loading patterns, redundancy integrity, and human-factor vulnerability. A generator with excellent test records and robust fuel management may have a different probability profile from a similarly aged generator with repeated deficiencies, contamination risk, or cooling system issues.
A practical probability scale might rate each asset from 1 to 5 based on: current condition, failures in the last 24 months, PM completion and quality, parts availability, and stress/load exposure. The important point is consistency. If one hospital uses age alone and another uses mostly subjective opinion, the criticality output becomes unreliable. A structured probability model gives engineering leaders a defensible rationale for why two assets with similar consequences receive different risk rankings. It also supports DNV’s expectation that organizations measure occurrences, analyze them for patterns and trends, and use that information to improve the physical environment management system.
In hospitals, not every high-severity asset is equally exposed because some failures are more visible, more controllable, or more recoverable than others. That is why many organizations add a third dimension beyond consequence and probability: control effectiveness or detectability/recoverability. This is entirely consistent with Greeman’s emphasis on practical risk treatment and decision-making, and it aligns with DNV’s broader continuous-improvement mindset. A utility asset with real-time monitoring, trained response teams, spare parts on site, clear downtime procedures, and tested redundancy is materially different from one whose deterioration is largely hidden until failure occurs.
Consider two assets: a normal power panel serving administrative areas, and a Category 1 automatic transfer switch serving ICU loads. The ATS has much higher consequences if it fails, but if it is under tight maintenance control, periodically tested, remotely alarmed, and supported by redundant emergency power architecture, the organization may have strong mitigation. Conversely, a lower-category domestic water booster with poor monitoring and repeated nuisance failures might create a higher near-term operational burden than its initial category suggests. This is why mature hospital programs often calculate inherent risk first (consequence × probability), then apply a control factor to determine residual risk. That helps leadership distinguish what is structurally critical from what is currently most fragile.
A practical hospital utility risk model can be built in five steps. First, identify the asset and its clinical function. Second, assign the NFPA 99-aligned consequence category. Third, score probability of failure using defined asset drivers. Fourth, score control effectiveness or recoverability. Fifth, calculate risk and place the asset into a response tier. This preserves regulatory alignment while giving the organization something operationally useful.
One example formula is:
Residual Risk = Consequence × Probability × Control Modifier
Where:
Using that approach:
This kind of method is intentionally simple. It is not trying to replace engineering judgment; it is creating a disciplined way to apply that judgment across hundreds or thousands of utility assets.
Start with the utility systems most closely tied to patient harm and operational continuity. In most hospitals, that includes essential electrical distribution, generators, ATSs, fuel systems, medical gas source equipment and distribution, air-handling systems supporting critical spaces, chilled water and cooling where clinically necessary, boilers and steam where needed for sterilization or heat, domestic water systems, nurse call power dependencies, and selected controls/automation components whose failure can disable a broader system. NFPA 99 provides the risk-based framework for system categorization, while DNV’s physical environment requirements reinforce that facilities, supplies, and equipment must be maintained to ensure acceptable safety and quality.
A good rule is to assess first the assets that meet one or more of these criteria: they support Category 1 functions; they have single-point-of-failure characteristics; they have known reliability concerns; they are in aging or obsolete condition; or their loss would create broad service-line disruption. Hospitals that try to assess every asset with the same intensity usually stall. A tiered rollout is more effective: start with mission-critical utilities, then move to secondary support systems, then incorporate remaining building services into the same framework over time.
One of the biggest mistakes in healthcare infrastructure risk ranking is treating it as a facilities-only activity. Greeman’s method emphasizes organizational context, and hospital context is inherently multidisciplinary. Facilities leaders should convene operations, nursing, perioperative leadership, infection prevention, respiratory therapy, biomedical/HTM, quality, emergency management, and finance when scoring major utility assets. DNV’s quality-oriented NIAHO approach also supports this broader governance model by expecting defined processes, evaluation, oversight, and evidence of improvement rather than siloed technical decisions.
This matters because risk is experienced differently across stakeholders. A temporary HVAC disruption in a nonclinical office area is fundamentally different from pressure instability in an airborne infection isolation room suite. A water interruption may appear manageable from a maintenance perspective but may create immediate implications for hand hygiene, sterile processing, dialysis, food service, and regulatory reporting. Cross-functional scoring improves accuracy, creates shared ownership of mitigation plans, and makes it much easier to defend the final rankings in executive review or during survey.
The purpose of a utility asset criticality program is not to generate colorful dashboards. It is to drive decisions. Greeman’s framework explicitly aims to support asset decision-making and treatment options, and DNV’s model emphasizes ongoing measurement, analysis, and system improvement. Therefore, every assessed asset should land in an action pathway: capital replacement, enhanced PM/PdM, contingency planning, spare-parts stocking, redundancy improvement, policy revision, training, alarm strategy enhancement, or no immediate action beyond routine monitoring.
For example, a high-risk oxygen source subsystem might trigger immediate verification of source redundancy, updated shutdown procedures, drill review, OEM service confirmation, and executive escalation for capital reserve. A moderate-risk steam trap or hot water loop issue might trigger targeted repairs, trend monitoring, and inclusion in the next budget cycle. A low-risk comfort system asset might remain on standard maintenance frequency. When leaders can see that the score leads to a specific response, the assessment becomes far more than a compliance artifact—it becomes a management system.
The strongest hospital utility asset assessments combine three perspectives. Clinical perspective: What harm could occur if this utility function fails? Technical perspective: How likely is that failure, given asset condition and performance? Operational perspective: How quickly can the hospital detect, respond, and recover? NFPA 99 gives the clinical risk foundation. Greeman’s Risk-Based Asset Criticality Assessment Handbook provides the logic for turning organizational and asset drivers into real risk information. DNV’s NIAHO structure ensures the whole process is documented, analyzed, governed, and continuously improved. Used together, they can help hospitals move from reactive maintenance lists to a defensible, survey-ready, leadership-grade utility risk program.
Save time & resources with Soleran Integrated Compliance Management.
.svg)
.svg)
.png)
.jpg)
.jpg)
.svg)
