How to get information you can trust and apply from your computerized maintenance management system

By Ted Cohen, MS, FACCE, and Matt Baretich, PE, PhD

Data and data management are at the heart of all HTM-oriented computerized maintenance management system (CMMS) software. After all, without accurate and complete data, a CMMS is not going to be of much use to the HTM department. Here, we will explain how a modern CMMS can help HTM departments collect and apply high-quality data—a subject explored in depth in our new AAMI book, Computerized Maintenance Management Systems for Healthcare Technology Management, 3rd Edition.

A CMMS is like a medical record for equipment, and all HTM staff are responsible for assuring that the data they enter and update are complete and accurate. Consider the old maxim, “garbage in, garbage out”—it applies to a CMMS as much as to other databases. Unfortunately, in our HTM consulting work, we commonly see incomplete CMMS data, which can make it impossible to produce useful reports—thus leading to poor decision-making.

Creating High-Quality Data

Data quality depends on several factors. Here are the top six:

1. Availability of HTM-relevant data sources: Obviously, if the data are not available to HTM staff, then staff members can’t be responsible for entering it into the CMMS. For example, medical device acquisition cost data, which should be a completed field on every equipment asset record, requires purchase order or invoice information in order to capture the device’s price. Many HTM departments are kept out of that loop.

2. HTM employee accountability: Documentation quality begins at the source: the employees. CMMS training and documentation expectations and guidelines should be provided to every HTM employee. And assessment of documentation quality should be included in employee performance appraisals.

3. CMMS data standardization: Operation of a high-data-quality CMMS depends on multiple levels of data field standards. These include fields defined by the CMMS vendor; fields defined within the healthcare organization; and fields that use external, “standardized” definitions. Policies should indicate which data definitions HTM staff should use. Case in point: Fields that indicate which department “owns” a medical device can use the organization’s own chart of accounts.

Further, device type fields can use ECRI Institute’s Universal Medical Device Nomenclature System (UMDNS). HTM supervisory personnel or the CMMS database administrator should be responsible for managing (adding, deleting, and updating) these and other foundation data tables (e.g., department, building, manufacturer, and vendor lists).

4. Referential integrity: For the foundational data, both the underlying database structure and the CMMS application error-checking capability are key to ensuring high data quality. The database should include appropriate referential integrity in order to make sure that related fields do not have any “orphan” data. For example, every equipment record should have a manufacturer reference, and every manufacturer reference should have a manufacturer name, address, etc.

5. Field definitions: Each field should have a written definition so every HTM staff member uses the field the same way. For instance, for a network-connected medical device, what is the definition of the “port” field on the IT data section of the asset record? Is it the physical port where the Ethernet cable plugs into the wall or the virtual port used for communication with the electronic medical record? Both “ports” are important data, but they serve different purposes.

6. Maintenance activity coding: When maintenance data must be shared or compared, it’s imperative that maintenance activities are consistently and accurately coded. Although dropdown lists and other coding techniques help standardize data, it’s sometimes at the expense of detail. Definitions are paramount to make sure “apples to apples” comparisons are possible.

Error Checking

Data integrity starts at data entry. “Real-time” error checking by the CMMS warns (soft stop) or stops (hard stop) data entry whenever a potential problem is recognized. Some data errors are easy to identify—such as an alpha character in a numeric-only equipment control number or future work order completion dates—whereas others are more difficult to spot.

Below are some examples:

  • Data types: The CMMS should automatically ensure that each field has the appropriate data type entered (e.g., string, number, dollar amount, date).
  • Range constraints: Most fields have logical ranges, so constraints can be configured to make sure the data entry is stopped or a warning is issued if a value is out of the expected range. For example, a range check for a vendor hourly rate field could be set for a minimum of $50 to a maximum of $999 per hour to catch obviously erroneous data entry.
  • Date checks: Dates (and times) need to have a consistent format throughout the CMMS. Some dates, such as the next PM due date, must be in the future when initially entered. Most other dates should be current or in the past when entered (e.g., work order completion date).
  • Multiple-field error checking: Multiple-field error checking is more complex, often requiring special CMMS configuration or customization. For example, suppose that downtime is a required field when an asset is a “critical system,” but optional if it is not. Multiple-field error checking would assure that downtime is always recorded on critical-system repair work orders.
  • HTM business rules: A healthcare organization may have internal requirements that mandate certain rules in the CMMS. For instance, business rules may authorize different levels of purchasing authority for parts and services for different HTM personnel, based on the dollar amount of the order (e.g., a BMET is allowed to order up to $1,000 without additional approval, whereas a supervisor is allowed to order up to $5,000).

In addition to automatic error checking, periodic audits are key to maintaining data quality. After all, some data problems that require data aggregation cannot be caught immediately at data entry—such as recognizing that an insufficient number of labor hours have been documented on work orders compared to payroll system-related data (e.g., time card entries). Sampling audits by supervisors can also check for problems that are difficult to automate, such as verifying that work orders are accurate and documented in sufficient detail.

Using High-Quality Data

One of the critical success factors for HTM is performance monitoring and performance improvement. We need to be able to compare our own performance over time, our performance relative to the performance of other healthcare facilities within our own healthcare system, and our system’s performance relative to the performance of other systems. A fundamental requirement for those types of benchmarking is data consistency.

Unfortunately, the HTM profession has done a poor job of standardizing field definitions and establishing data collection guidelines. Although there is not necessarily a right or wrong definition for any given field, this inconsistent environment makes meaningful comparisons very difficult.

Financial metrics are one area for which a considerable amount of work has been completed. For example, the cost of service ratio (COSR) metric adds all internal expenses, plus all external expenses for medical equipment maintenance, and divides the sum by the total acquisition cost of the medical equipment. COSR isn’t specific to HTM, however—it’s widely used throughout the service industry.

Internal costs consist of personnel expenses (including benefits), along with other expenses included in the HTM department budget (stock parts and supplies, test equipment, staff training, etc.). External expenses comprise vendor parts, equipment service contracts, and fee-for-service expenditures.

One rule of thumb for COSR calculation is “if you include it in the denominator, include it in the numerator,” and vice versa. In other words, have a complete inventory of equipment you support and capture all the costs of supporting that exact inventory.

Furthermore, the effective hourly rate metric is calculated as internal costs (defined above) divided by the number of technician hours documented on work orders. This is a useful metric for comparing the hourly cost of in-house service to the hourly costs of vendor service for a specific type of medical equipment support. It can also help you decide whether to use in-house resources or external resources to get the support you need as economically as possible.

For further definition of these terms and their corresponding fields, see Computerized Maintenance Management Systems for Healthcare Technology Management or AAMI’s HTM Benchmarking Guide, which is available online here.

Implementing AEM Strategies

Within limitations, the Center for Medicare and Medicaid Services, the Joint Commission, and other accrediting organizations allow the use of an alternative equipment maintenance (AEM) strategy as a substitute for manufacturer recommendations regarding PM activities and frequencies. AEMs are attractive because, when well designed, they can ease our PM workload without reducing patient safety. So, how can high-quality CMMS data help?

First, not all types of medical equipment are eligible for AEM strategies—for instance, imaging equipment and lasers are not AEM-eligible. Fortunately, the CMMS can be configured to indicate whether or not an asset is eligible for AEM and whether or not an AEM is being used for that asset. Then, if an attempt is made to inappropriately specify an AEM strategy for a particular device, the CMMS can issue a warning or prohibit the action.

The CMMS can also help identify which types of equipment might be good candidates for AEM. For example, the CMMS can provide data collection and reporting tools that track whether the rate of “PM-preventable” problems is low enough to warrant implementation of an AEM strategy. After AEM implementation, the CMMS can monitor the effectiveness of the AEM—determining, for instance, whether the failure rate for that model increased after adopting an AEM strategy.

To take advantage of the AEM option, we have to comply with accreditation standards and judiciously choose equipment for which an AEM strategy will save us time and money. To meet these criteria, we need good CMMS data—and plenty of it.

Accumulating Data for Evidence-Based Maintenance

CMMS databases are the primary repositories of repair data for hospital-based medical devices. If we could standardize our practices and terminology for medical equipment failure data, the HTM profession could accumulate evidence about what works best in medical equipment maintenance.

One of the most promising methodologies is reliability-centered maintenance (RCM), a practice developed by the airline industry in the ’60s and ’70s that uses equipment failure data to optimize maintenance activities and frequencies. In the airline sector, RCM has proven very successful in reducing maintenance time and expenses while maintaining or increasing safety. Even so, the adoption of RCM principles in HTM has been very limited.

A key reason for this? Moving ahead with RCM in HTM requires aggregation and sharing of medical equipment failure data. AAMI, for instance, has established an RCM Task Force that is working to collect and analyze failure data, focusing first on high-risk and life-support devices (

And a subgroup of the RCM Task Force is working on standardizing definitions for use in CMMS databases. After all, if the data in this repository are to be used for evidence-based maintenance, they must be accurate and consistent.

Good Data In, Good Information Out

For a CMMS to be effective, it must be easy to use. Luckily, modern CMMS technologies allow the use of smartphones, tablets, and laptops—with pick lists, autocomplete text entry, voice to text, and other modern data-entry techniques simplifying technology use. What’s more, reports can be set up to run at regular intervals and dashboards can automatically update in near real-time.

Further, barcoding of equipment and parts bins can improve accuracy and efficiency, and interfaces to RTLS provide real-time location updates. Also, Software as a Service (SaaS) options (externally-hosted CMMSs) can improve IT security and relieve the HTM department of some routine IT-related tasks, such as backups.

All of these innovations allow HTM departments to focus on improving the safety and efficacy of medical technology, rather than wrestling with cumbersome CMMS software. After all, the CMMS should be an effective management tool—one that makes it easy to enter and maintain good data, as well as generate useful information you can trust.

Ted Cohen is an HTM consultant and former manager of clinical engineering at UC Davis Health, where he currently serves as a part-time clinical engineer. Matt Baretich is the founder of Baretich Engineering and provides HTM consulting and incident investigation services. For more information, contact chief editor Keri Forsythe-Stephens at [email protected].