Risk Assessment of Medical Equipment -...

ASHE Monograph

Risk Assessment of Medical Equipment

John Collins, FASHE, HFDP

© 2015 ASHE

The American Society for Healthcare Engineering (ASHE)of the American Hospital Association155 North Wacker Drive, Suite 400

Chicago, IL 60606312-422-3800

[email protected] www.ashe.org

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Disclaimer

This document was prepared on a volunteer basis as a contribution to ASHE and is provided by ASHE as a service to its members. The information provided may not apply to a reader’s specific situation and is not a substitute for application of the reader’s own independent judgment or the advice of a competent professional. Neither ASHE nor any author makes any guaranty or warranty as to the accuracy or completeness of any information contained in this document. ASHE and the authors disclaim liability for personal injury, property damage, or other damages of any kind, whether special, indirect, consequential, or compensatory, that may result directly or indirectly from use of or reliance on this document.

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Contents

Risk Assessment of Medical Equipment 1

Introduction 1

Part 1 3

Method 4Results 5Conclusion 7

Part 2: Physical Risk Assessment of Medical Equipment 9

Introduction 9Regulatory History 10Method 11Distinguishing Data 12Defining Data YY Type of Event 14Categories of Patient Outcomes 15Patient Death 16Patient Injury 18Device At Fault 19Analysis 19A New Scoring Protocol 20Conclusion 26

Part 3: An Analysis of Parts Replaced in a Medical Equipment Management Program 27

Introduction 27Method 27

A S H E M o n o g r a p hvi

Results 28Discussion 34Applicability of this Approach in other Settings 34Conclusion 35

Appendix A 37

List of Device Types with R > 0.5 37

References 39

1

Risk Assessment of Medical Equipment

Introduction

Risk assessment of medical equipment is an integral part of the Joint Commission’s Environment of Care Management Plans. Such assessments consider the potential physical risks associated with the equipment’s use, function, and incident history. The assessment cannot be anecdotal; it must be based on data. “A golden rule of insurance is that you do not underwrite a risk that cannot be quantified” (Fennigkoh and Smith 1989). Computerized medical maintenance systems have made available a wealth of data that can be used to quantify risk assessment in medical equipment.

This monograph is divided into three parts, which together present a frame-work for an efficient risk assessment plan. Part I discusses maintenance risk assessment of medical equipment, comparing the use of computerized risk management databases with qualitative scoring of risk assessment param-eters. Part II, on physical risk assessment of medical equipment, looks at different approaches to assigning risk scores to devices and analyzing that data. Part III is an analysis of parts replacement in a specific medical manage-ment program.

3

Part 1

Most maintenance programs still use some variation of the seminal scoring process devised by Fennigkoh and Smith (1989) as a basis for their medical equipment risk assessment. However, this scoring process is qualitative; it is not intended to be used for risk assessment. It was written to meet an earlier Joint Commission requirement to distinguish between medical equipment to be included in a preventive maintenance (PM) program and equipment to be repaired when necessary. This second group was deemed minor in terms of function related to direct patient care, posed no physical risk, and had low maintenance requirements, according to the manufacturer.

In the past, few departments used computerized maintenance programs, so in-house repair data was infrequently used as a factor in scoring. Instead, the scoring process evolved into a kind of risk assessment of medical equip-ment but without quantifying the three parameters: use, function, and incident history. This meant that “risk assessments” were educated guesses about which equipment might cause bodily harm or death to a patient and which equipment requires PM. Subsequent reports have redefined the equip-ment function in a more realistic clinical manner using FDA incident reports to quantify physical risk (Collins and Dysko 2001). For example, in 1996 Capuano and Koritko used a maintenance rating in which the score for each device depended on the number of items checked off a list containing the following items: electronic adjustment, mechanical adjustment, moving parts, regular parts replacement, significant user intervention, organizational requirements, and regular cleaning required. Gullikson and colleagues (1996) created a maintenance facto, based on average staff hours per device per year and equipment type. Fennigkoh and Lagerman updated the original article in 1997, stating, “It is difficult to quantify the maintenance requirements of

A S H E M o n o g r a p h4

device types.” They cited the variety of models and manufacturers and the frequency of use as factors that make such quantification difficult.

Part I of this monograph describes a simple method for measuring the main-tenance parameter that, together with Part II on measuring the physical risk parameter and the readjustment of the function parameter scoring, produces an equipment risk assessment score based on data.

Method

Initially, a retrospective analysis of 24 months of repairs for all medical equip-ment in five hospitals plus a high-tech outpatient facility and a home health unit was done for all repairs costing more than $50. Four more hospitals were added over the next 18 months, and the final analysis covers a four-year period and more than 3,500 different device models, or about 50,000 devices. Since each health care facility was independent prior to this analysis, each used a variety of manufacturers and models for device types.

The analysis looked at 349 different device types and 3,518 models. The $50 figure was chosen to eliminate repairs for such items as power cords and ECG leads. Repairs due to abuse were not included. Repairs done by outside ven-dors were included, excluding travel time. Repair times were kept for each individual equipment model in the inventory and grouped by equipment description. The fields in the database are description, model, manufacturer, quantity, and facility. The work histories of all models from all manufactur-ers were tabulated by quantity, total repair cost, and number of months in use. The total repair time for each device type was divided by the number of devices and the number of months in use. This number multiplied by 12 gives an annual normalized repair time of hours/device/year.

If a device was retired and had significant work histories in terms of cost, as is common with radiology equipment, it was kept in the database as a separate entry with a constant month total (from day 1 to retirement day) and was averaged in with other models of the same type that were in use. For instance, if a CT scanner with a repair history of 36 months was replaced, the repair cost parameter of that device would become a constant and would be aver-aged in with the remaining CT scanners, which would continue to have new repair data incorporated monthly. Repair work data was tabulated monthly.

R i s k A s s e s s m e n t o f M e d i c a l E q u i p m e n t 5

Results

The repair parameter was arbitrarily divided into a range from 0 to 5 to cor-respond with the maintenance scoring in the original Fennigkoh and Smith paper. Those scores range from 5 (extensive), to 3 (average), to 1 (minimal). Table 1.1 shows the arbitrary grouping of the scores versus R (repair time/device/year).

Table 1.1 Maintenance Requirement Scoring Level versus Parameter R (Repair Time/Device/Year)

Maintenance Score (R) Repair Time/Device/Year

5 >20

4 >10<20

3 >5<10

2 >2.5<5

1 >.5<2.5

0 <.5

Figure 1.1 R v. Device Types

Radio

graphic

, cat

./vas

c.

Camera

, gam

ma

Analyz

er, urin

e

OB d

ata

man

agem

ent sys

tem

Apheresis

unit

Inje

ctor,

angio

graphic

Phacoem

ulsifier

Incubat

or, in

fant,

transp

ort

Analyz

er, pulm

onary f

unction

Pleth

ysm

ograph

Monito

r, NIB

P

Anesthesia

unit

Incubat

or, in

fant

Pump, s

yrin

ge

Light d

evice

Trac

tion u

nit

Generato

r, le

sion

Monito

r, car

diac o

utput

Analyz

er, blo

od cultu

re

Dynam

omete

r

Lase

r, KTP

Feta

l monito

r

Surgic

al lig

ht

14

12

10

8

6

4

2

0

The normalized parameter of repair hours/device/year versus the device descriptions. This shows that the majority (77 percent) of devices in the equipment program have no significant repair times (R < 0.5) for the 48-month period. Note: The x-axis shows every 10th description and is only meant to show graphically that most devices have essentially no repair times over the four years.


The data from all of the equipment was divided into device type and averaged over the four year time period. Figure 1.1 is a graphic representation of the nor-malized parameter of average repair time per device per year versus the device type. Appendix A is a table of the individual device types in detail for R> .5.

Figure 1.2 R (Worst Case) versus Device Type

The normalized parameter of repair hours/device/year versus the device descriptions for the maximum value of each device type. This shows that the majority (58 percent) of devices in an equipment program have no significant repair times (R < 0.5) for the 48-month period. Note: the x-axis shows every 10th description and is only meant to show graphically that most devices have essentially no repair times over the four years.

Steril

izer,

pericetic

acid

Bone densit

omete

r

Steril

izer,

ETO

Scanner,

ultras

onic

Tran

sducer,

ultras

ound

OB d

ata

mgm

t. sy

stem

Defibrilat

or

Oxim

eter

Phototh

erapy

unit

Audiom

eter

Mic

rophone

Nitrous o

xide d

elivery

syst

em

Chillin

g unit,

cold

pac

k

Aspira

tor

Analyz

er, m

iddle

ear

Breat

h anal

yzer

Equip.,

lab, o

ther

Fibril

lato

r

Lary

ngoscope

Throm

bectom

y sy

stem

100

90

80

70

60

50

40

30

20

10

0

Figure 1.2 shows the same devices using the maximum or “worst case” value of the repair parameter for each device type instead of an average. In this case the majority of devices having a repair parameter less than 0.5 fall to 58 percent.

Before the development of this scoring approach, maintenance scoring was done in the traditional method of making educated guesses as to the level of maintenance repairs a device probably required or by manufacturer’s recom-mendations, which for the most part were not quantified either. Figure 1.3 shows a comparison of the educated guesses of maintenance repairs from a group of clinical engineers with about 75 collective years of experience compared with the actual quantitative results for both the average repair parameter for each device and the maximum or worst case repair parameter for each device type.


There is not much difference in the scoring between the quantitative average repair parameter scoring and the quantitative maximum repair parameter. The devices change values along the x axis in parameter from average to maxi-mum but the number of devices that have an R value greater than 0.5 is still a minority of the total number of device types. More important, both quanti-tative results show that the scores used in the traditional qualitative approach when used in the usual risk assessment are too high for most devices.

Conclusion

The repairs for the majority of medical equipment included in this study over a four-year period were minimal and much less than would be predicted based on an educated guess. Devices that did need repairs were those with moving parts and complex designs, which is to be expected. The original five hospitals, home health care unit, and high-tech outpatient unit used as a start date for data collection the day that they were first merged from independent status as parts of separate hospitals into a unified corporate clin-ical engineering department. Previous work histories were not used because of unavailability or lack of confidence in their accuracy. Baseline data was

Comparison of the scoring levels for all of the medical equipment, from the original guestimate when the program started to the average quantitative scoring and the worst case or maximum scoring parameter for each device type.

Figure 1.3 Maintenance Score versus Device Type

Worst Case Score

Quantitative Score

Original Guestim

ate

Maintenance Score

Number of

Device Types

300

250

200

150

50

0


obtained from the first two years of work histories for repairs that cost more than $50 under the new program. Work histories, therefore, were a snapshot in time of most devices since they had been in operation before the corporate merger. In the subsequent two years, with the gradual addition of four more hospitals into the program and the addition and retirement of numerous devices in all hospitals, the monthly scores for device categories were remark-ably constant. For example, infusion pumps had an initial score of 1 based on the two-year retrospective analysis; at the end of four years, the score remained 1. CT scanners started with a score of 3 and remained at 3 after the fourth year. Part of the reason for the constancy was the large quantity of equipment compared a single hospital. A single hospital, after establish-ing a baseline, should notice a more pronounced change in scoring of some devices (such as centrifuges) as they age. In our case, the number of devices tended to blur such distinctions. The information was useful in comparing the different manufacturers and models of a particular device over time. One example was urine analyzers. Of three manufacturers and seven models, the three models associated with a single manufacturer showed markedly greater repair parameters than the other two. This meant that the total risk assess-ment score moved this manufacturer’s devices into a different, shorter PM schedule (e.g., every six months instead of yearly). In these cases, our main-tenance program software had the flexibility to allow a different time interval for particular models of a device type in the PM schedules.

With the almost universal use of computerized database systems for manag-ing medical equipment, the risk assessment needed to manage the equipment can be done much more realistically than it could by relying on qualitative scoring of the risk assessment parameters. This method provides an economi-cal, reliable means of predicting which equipment may be eliminated from a PM program, or increasing the intervals between their scheduled PMs. Many types of equipment still included in PM programs can be eliminated by increasing the intervals for their scheduled preventive maintenance.

9

Part 2: Physical Risk Assessment of Medical Equipment

Introduction

All medical equipment assessment programs have one overriding objective: to eliminate risk to patients from malfunctioning medical devices.

The protocol most widely used by most hospitals employs the Fennigkoh and Smith (1989) approach to meet a Joint Commission requirement that each hospital have an equipment management program. Fennigkoh and Smith (1989) assign each device a numeric value according to its function, its phys-ical risk, and maintenance requirements to reach an equipment management number (EM). An arbitrary EM of 12 is used to determine inclusion in a program.

While these simple criteria may have worked to segregate classes of equip-ment for maintenance purposes, they are increasingly being used to predict the overall risk to patients from all medical equipment (Capuano and Koritko 1996; Fennigkoh and Langerman 1997; Keil 1997; James 1999; Wang and Levenson 2000; Steifel 2002).

This approach, however, has one major flaw: it is qualitative, relying on sub-jectively assigned numbers. For example, physical risk is scored as follows:


5—potential patient death

4—potential patient injury

3—inappropriate therapy

2—equipment damage

1—no significant risk

A more accurate predictor of potential physical risk to patients may be achieved by analyzing verifiable Food and Drug Administration (FDA) data, then assigning a score to each medical device based on reported adverse events implicating that particular device.

A description follows of using this data-based method to quantify the physi-cal risk for medical equipment that would be included in a biomedical equipment program. It demonstrates how the rescoring of this factor alone can significantly reduce the total risk score of certain equipment and reduce preventive maintenance costs by focusing on those devices whose operation poses physical risk to patients.

Regulatory History

In 1976, the Medical Device Amendments (CFR 1976) gave the FDA specific authority to regulate medical devices, requiring manufacturers to submit a baseline report on a device model whenever one of their products is repaired or replaced.

In 1984, the Medical Device Reporting regulation (CFR 1995) came into effect. Manufacturers must submit reports whenever they become aware of information that reasonably suggests that one of their devices may have caused or contributed to a death or serious injury, or has malfunctioned and would likely cause or contribute to a death or serious injury if the malfunc-tion were to recur. These reports were submitted on form 3417 (MDR). Compliance was fairly low, prompting Congress to pass the Safe Medical Devices Act in 1990. This mandates that users as well as manufacturers of medical devices must report adverse events (CFR 1990). These reports are submitted using revised forms called Medwatch (3500/3500A), which are for voluntary and mandatory reporting respectively. In 1997, the FDA started to make information from the Center for Devices and Radiological Health (CDRH) available online. By 1998, they had published their com-


plete Medical Device Report (MDR) and the ongoing Manufacturer and User Device Experience (MAUDE) databases. At publication, there are over two million records of incident reports involving medical devices. This data was analyzed to quantify the physical risk component and make it a reliable measurement as part of the overall risk assessment of those devices.

Method

Both databases (MDR data for 1984 to 1997 and MAUDE data for 1991 through 2001) were downloaded for analysis. The MDR data consists of 17 fields with relevant information in 9 of the fields in a standard linear database:

1. Access type and number

2. Date received

3. Product description

4. Manufacturer name code

5. Manufacturer name

6. Street address

7. City

8. State

9. Zip code

10. Report type

11. Model number

12. Catalog number

13. FDA panel code

14. FDA product code

15. Event description type

16. Event description

17. Closeout text

The MAUDE data has four different types of files linked by a MDR report key as a relational database. The files are called master event, device, patient, and text data, with a total of 126 fields in four files. The relevant fields are:

1. MDR event key


2. Date received

3. Adverse event

4. Product problem

5. Health professional

6. Remedial action

7. Brand name

8. Generic name

9. Manufacturer name

10. Model

11. Patient outcome

12. Date report

13. Text

The MDR event key is the common field linking the four files.

Distinguishing Data

Since the FDA definition of a medical device encompasses more than those devices of concern to clinical engineers managing an equipment maintenance program, it was necessary to add a field (device filter) to flag equipment considered relevant and exclude devices such as disposables, prostheses, and bone cement. The FDA’s definition of a medical device is “an instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent, or other similar article that is intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment or prevention of dis-ease” (CFR 1998).

Each of the records was reviewed using as many fields as necessary, since quite a few of the individual records were either incomplete or were inaccurate, to determine whether the equipment would be of interest in a hospital medi-cal equipment management program (biomedical equipment). The relevant records were flagged with an entry in the additional device filter field. The MDR files for the time period studied include 607,163 records, with 81,984 (13.9 percent) identified as relevant (see Figure 2.1). The MAUDE device file includes 196,643 records, but only 22,313 (11.3 percent) were identified as relevant (Figure 2.2).


Figure 2.1 MDR Reports versus Biomedical Equipment

1984

1986

1988

1990

1992

1994

1996

Reports

Year

120000

100000

80000

60000

40000

20000

0

MDR reports versus actual number of medical equipment identified as relevant to an equipment management program. The percentage of reports involving medical equipment versus the total number of incident reports averages about 14 percent per year.

Figure 2.2 MAUDE Reports versus Biomedical Equipment

Reports

Reports

Biomedical Equipment

Biomedical Equipment

1991

1993

1995

1997

1999

2001

Reports

Year

80000

60000

4000

2000

0

MAUDE reports versus actual number of pieces of medical equipment identified as relevant in an equipment management program. The percentage of reports involving medical equipment versus the total number of incident reports averages about 11 percent per year.


Defining Data by Type of Event

The event description type field in the MDR reports contains a descrip-tion of the reason for the incident report. These reasons are death, serious injury, or malfunction. The equivalent field in the MAUDE reports is the patient outcome field. There are 10 categories: life-threatening, hospitaliza-tion, disability, congenital anomaly, required intervention, other, unknown, no information, not applicable, and death. Life threatening, disability, and hospitalization were combined and classified as serious injury to agree with the MDR serious injury field. Malfunction is a combination of the categories required intervention and other, neither of which resulted in harm to the patient. Although the patient outcome in the MAUDE reports is specific, this does not mean the equipment listed in the report was the cause of the incident. To determine if the equipment was at fault in an incident, it is nec-essary to analyze the text field in the Text file. Figure 2.3 shows two examples of the contents of a text field.

Figure 2.3

Text

MISFIRING ON THE T-WAVE WHILE IN SYNCHRONIZED MODE FOR CARDIOVERSION USE. DEVICE LABELED FOR SINGLE USE. PATIENT MEDICAL STATUS PRIOR TO EVENT: SATISFACTORY CONDITION. THERE WAS NOT MULTIPLE PATIENT INVOLVEMENT. DEVICE SERVICED IN ACCORDANCE WITH SERVICE SCHEDULE. DATE LAST SERVICED: 01-FEB-95. SERVICE PROVIDED BY: USER FACILITY BIOMEDICAL/BIOENGINEERING DEPARTMENT. SERVICE RECORDS AVAILABLE. IMMINENT HAZARD TO PUBLIC HEALTH CLAIMED. DEVICE USED AS LABELED/INTENDED. DEVICE WAS EVALUATED AFTER THE EVENT. METHOD OF EVALUATION: ACTUAL DEVICE INVOLVED IN INCIDENT WAS EVALUATED, A DEVICE FROM SAME LOT WAS EVALUATED, OTHER, OTHER. RESULTS OF EVALUATION: COMPONENT FAILURE. CONCLUSION: DEVICE FAILED DURING ASSEMBLY. CERTAINTY OF DEVICE AS CAUSE OF OR CONTRIBUTOR TO EVENT: YES. CORRECTIVE ACTIONS: DEVICE REPAIRED AND PUT BACK IN SERVICE. THE DEVICE WAS NOT DESTROYED/DISPOSED OF.

MDR Report Key Date Received Text Patient Outcome

341768 7/10/2001 FAULT VERIFIED. FOUND FAULT TO BE WITH SYSTEM BOARD. REPLACED SYSTEM BOARD. DEVICE WAS RECERTIFIED AND RETURNED TO LOANER STOCK.

1,D;2,O

Figure 2.3. Examples of data from the text field in the Text file of the MAUDE reports from two different reports. The top portion is a text field from a MAUDE report showing that the field is a compilation of all the text entries from the entire Form 3500. It shows that the cause of the incident was a component failure of the device. The bottom part illustrates a query that shows that a device had a faulty circuit board and a patient death was the outcome.


Categories of Patient Outcomes

These records, for the MDR and MAUDE reports, were then categorized by patient outcomes: death, serious injury, and malfunction. If a record showed that the device was at fault, the record was identified in the device filter field. The MDR data, which has 81,984 relevant devices, was found to have 9,405 (11 percent) devices, that were identified as at fault in incident reports. To date, the MAUDE data has 45,213 identified devices, with 437 (1 percent) at fault. A separate spreadsheet containing 435 equipment descriptions from the medical equipment program was used to tally the individual counts for each equipment description on a yearly basis, along with the patient out-come. Figure 2.4 shows a portion of the spreadsheet for the MDR data. The counts for each device fault were entered into the spreadsheet data next to the appropriate patient outcome columns in the column labeled device fault.

Figure 2.4

Year 1984 1984 1984 1984 1984 1984

Description Total Death Device Fault

Serious Injury

Device Fault

Malfunction

Absorber

Aerosol tent

Alarm, bed exit

Alarm, low pressure

Analyzer, blood gas

Analyzer, chemistry 1 1

Analyzer, CO2

Analyzer, coagulation

Analyzer, electrolyte

Analyzer, hematology

Analyzer, immunoassay

Analyzer, microbiology

Analyzer, pH

Analyzer, pulmonary function

Analyzer, urine

Anesthesia unit 2 1 1

Part of the spreadsheet of the MDR data count used to categorize records by device, year, and reason for the report. The Description column lists 265 devices normally included in an equipment management program. The other columns contain the counts for each reason and a Device Fault column.


Patient Death

In Figures 2.5 and 2.6, the counts for patient outcome of death are shown by the specific type of medical equipment that was at fault for the MDR and the MAUDE data, respectively. For instance, in Figure 2.6, the column for anesthesia unit shows the total count on a yearly basis. There were two reports in all the MDR data identified as an anesthesia unit being the cause of a patient death.

Figure 2.5 MDR Reports: Death—Equipment Fault

Defibrillator

VentilatorPum

p, infusion

Monitor, apnea

1984

1986

1988

1990

1992

Reports

Year

70

60

50

40

30

20

10

0

MDR reports for patient death caused by the medical equipment. The points for the defibrillator were shifted to the end of the graph so that other data would not be hidden. The data for the defibrillator refers to one manufacturer’s equipment. This defibrillator was the subject of an FDA Safety Alert on Jan. 26, 1994 (FDA 1994). The graph shows that the instances of equipment fault occurs in only a small number of equipment types out of 265 in an equipment management program. There are few consistent occurrences for a particular equipment type except for the defibrillator and the ventilator.


Figure 2.6 MAUDE Reports: Death—Equipment Fault

Ventilator

Pump, intra-aortic balloon

Pacemaker, external

Lift, patient

Defibrillator

Circulatory assist unit

Anesthesia unit

1991

1993

1995

1997

1999

2001

Reports

Year

10

8

6

4

2

0

MAUDE reports for patient death caused by the device. The only device that appears consistently is the defibrillator. However, in contrast to Figure 2.5, the defibrillator entries are not for a single manufacturer or model, and there are fewer occurrences. As with Figure 2.5, note that very few equipment types are involved, and except for the patient lift, they are life support equipment.


Patient Injury

For patient outcomes listed as serious injury, the same procedure was fol-lowed. Data was counted in the same manner for equipment that was at fault when the patient outcome was serious injury. Figure 2.7 shows those devices that were found to be at fault in the MAUDE reports where the patient out-come was listed as serious injury.

Figure 2.7 MAUDE Reports: Serious Injury—Equipment Fail

Ventilator

Surgical light

Pump, PCA

Pump, infusion

Pump, enteral

Lift, patient

Glucometer

Electrosurgical unit

Dialysis unit

Defibrillator

Anesthesia unit

1991

1993

1995

1997

1999

2001

Reports

Year

20

18

16

14

12

10

8

6

4

2

0

Serious injuries with device at fault. This is a graphical representation of the data in Table 2.1 where a score of 3 in the column labeled New under Physical Risk Score identifies those device types in this figure that consistently occur from 1992 to 2001.


Device At Fault

The last category was for those devices that were found to be at fault when there was no patient death or serious injury. The reports only indicated that a device had malfunctioned. Figure 2.8 shows these devices.

Figure 2.8 MAUDE Reports: Malfunction—Equipment Fault

Ventilator, HFJV

Ventilator

Pump, syringe

Pump, infusion

Monitor, apnea

Linear accelerator

Electrosurgical unit

Defibrillator

Camera, gam

ma

1992

1994

1996

1998

2000

Reports

Year

Device malfunction with device at fault. These devices appear in the MAUDE reports but they had not caused harm to the patient. They show that these devices that appear here on a regular basis should be accorded a physical risk score of 2 as shown in Table 2.1 because they have potential to cause harm even though none has been reported. Some of these devices, however, have higher physical risk scores since they appeared in reports for death or serious injury.

30

25

20

15

10

5

0

Analysis

One can draw the following conclusions from examining FDA data. First, most incident reports do not involve medical equipment. Analyzing over 800,000 incident reports from 1984 to 2001 reveals that only 13 percent of


those reports involve medical equipment that would be included in a bio-medical equipment program; the majority of the reports involve implants and disposables.

Second, to date, only 1.2 percent of the relevant reports show that medical equipment had been at fault in causing death or serious injury to patients. Third, there appears to be no consistent pattern implicating any particular manufacturer, except for one defibrillator manufacturer that was eventually the subject of an FDA Safety Alert in 1994.

Fourth, this analysis reveals that certain equipment—regardless of manu-facturer or model—does cause harm to patients because of certain defects. These reports appear on a regular basis from1984 to present. For example, the defibrillator is listed as cause of patient death in 13 of the 18 years of data and the ventilator in ten of those years.

Finally, by assigning a new scoring protocol to all medical equipment, one that more accurately predicts which of those devices cause death or serious injury to patients, the risk assessment can be made more focused and more efficient.

A New Scoring Protocol

Using the findings from analyzing the FDA data, the Fennigkoh and Smith scoring protocol may be modified as follows:

5— Score assigned to those devices found in current safety alerts or recalls or that have a record of preventive maintenance failure or incident reports generated in the hospital in which the equipment is found. A score of 5 is allotted to the occurrence of pertinent and current infor-mation about actual (incident report) or potential problems (recalls, alerts) with a particular device that is present in the user’s equipment inventory. It is meant to be a temporary score for the affected device. Once the problem is resolved, the status of the device can be moni-tored for a reasonable length of time for reoccurrences. If nothing further arises the device can be rescored to its original score.

4—For any equipment implicated in patient death in FDA data

3—For any equipment implicated in patient injury in FDA data

2—For devices that could cause patient injury

1—Devices that pose no physical risk


Comparing the old Fennigkoh and Smith protocol with the new, 32 devices are listed as likely to be a cause of patient death, while the FDA data shows only two devices that are consistently scored as causing patient deaths.

For serious injury, the old scoring has a total of 122 devices that could cause serious injury, while the FDA data shows only 27 devices that should be scored in this category. The 86 devices re-scored as 2 (could cause patient injury) in Table 2.1 appear in FDA reports but only because of apparent malfunctions. They were not involved in patient outcomes listed as death or serious injury. It seemed reasonable to choose this intermediate score between patient harm and a score of 1 (no physical risk), which was used for the remaining 100 devices in our medical equipment program that have never appeared in a FDA report.

Most programs use a total score of function, physical risk, and maintenance to set their preventive maintenance (PM) schedules. The usual criteria for conducting quarterly PMs are a score between 18 and 20. Semiannual PM schedules are followed for scores from 15 to 17. Annual PMs are used for scores from 12 to 14, and a score below 12 means that a device is not sched-uled for preventive maintenance.

By reassigning the physical risk scores based on FDA data, as shown in Table 2.1, we see that physical risk scores may change by as many as three points. Changing the overall risk score affects the PM interval. In all cases the risk total is reduced, and the schedule intervals increase. Changing the physical risk scores in the equipment inventory of a nine hospital network reduced the number of PMs by about 30 percent. On a hospital by hospital basis in this network, with the bed size of the hospitals ranging from 154 to 754, the PM reductions were all about the same.

Table 2.1 Physical Risk Scores

Device Description Old New

Aerosol tent 1 3

Alarm, bed exit 1 1

Analyzer, blood gas 2 3

Analyzer, chemistry 2 3

Analyzer, coagulation 2 3


Device Description (continued) Old New

Analyzer, electrolyte 2 3

Analyzer, hematology 2 3

Analyzer, immunoassay 2 3

Analyzer, microbiology 2 3

Analyzer, pH 2 3

Analyzer, pulmonary function 2 3

Analyzer, urine 2 3

Anesthesia unit 3 5

Aspirator 2 4

Aspirator, surgical 3 4

Automatic tourniquet 2 4

Blender, O2 2 3

Blood cell processor 2 3

Blood flow meter 1 2

Breathing unit, positive pressure 2 3

Camera, gamma 2 3

Centrifuge 2 3

Centrifuge, refrigerated 2 3

Chilling unit, cold pack 2 2

Compressor, air, high volume 2 5

Concentrator, O2 1 3

Counter, gamma 2 3

Cryosurgical unit 2 4

Defibrillator 4 5

Dialysis unit 3 5

Diathermy unit 2 4



ECG 2 3

Electrosurgical unit 3 4

EMG 2 3

Ergometer 2 4

Evoked potential system 2 4

Exercise equipment 2 4

Generator, lesion 3 4

Glucometer 2 3

Harmonic scalpel 3 4

Heart lung machine 3 5

Holter system 2 2

Humidifier, heated 2 4

Hydrocollator 2 2

Hydrotherapy unit 2 2

Hypothermia unit 3 4

Hypo/Hyperthermia unit 3 4

Incubator, infant 2 5

Incubator, infant, transp. 2 5

Incubator, laboratory 2 3

Injector, angiographic 3 5

Insufflator 3 4

Laser 3 4

Lift, patient 2 4

Linear accelerator 2 5

Lithotripter 3 4

Microbial growth monitor 1 2



Microscope, surgical 2 3

Monitor, airway gas 2 5

Monitor, airway pressure 2 5

Monitor, apnea 2 5

Monitor, cardiac output 2 3

Monitor, central station 2 5

Monitor, ECG 2 5

Monitor, fetal 2 5

Monitor, Holter 2 3

Monitor, intercranial pre. 2 5

Monitor, NIBP 2 4

Monitor, physiological, neonatal 2 5

Monitor, physiological, stress 2 5

Monitor, physiological 2 5

Monitor, pressure 2 5

MRI 2 3

Nebulizer 2 3

Oximeter 2 3

Pacemaker programmer 2 5

Pacemaker, external 3 5

Phacoemulsifier 3 4

Phototherapy unit 2 5

Plethysmograph 2 3

Pressure infusor 2 4

Pulse oximeter 3 3

Pump, athrombic 2 4



Pump, breast 1 2

Pump, enteral 3 3

Pump, infusion 3 5

Pump, intra-aortic balloon 3 5

Pump, PCA 3 5

Pump, syringe 3 5

Rad/Fluoro, general 2 4

Radiographic, cath./vasc. 2 4

Radiographic, chest 2 4

Radiographic, cysto. 2 4

Radiographic, dental 2 4

Radiographic, mammography 2 4

Radiographic, portable 2 4

Regulator, gas 1 1

Regulator, vacuum 1 1

Saw, surgical 2 4

Scale 2 3

Scanner, CT 2 4

Scanner, ultrasonic 2 3

Shaver, surgical 1 1

Spectrophotometer 1 1

Spirometer 2 3

Stainer, slide 1 3

Sterilizer, ETO 2 3

Sterilizer, steam 3 3

Stimulator, neuromuscular 2 4



Surgical light 1 2

Surgical motor drive unit 1 2

Telemetry system 2 5

Thermometer 2 3

Timer, coagulation 2 3

Tissue processor 1 2

Traction unit 2 4

Treadmill 2 4

Ultrasound therapy 2 4

Vaporizer 3 5

Ventilator 4 5

Ventilator, HFJV 2 5

Warmer, blood 2 4

Warmer, non-patient 1 1

Warmer, radiant 2 5

Washer/sterilizer 1 3

Partial listing of 127 devices scored for physical risk, with a comparison of old and new scores. The complete listing contains 265 devices from 9 hospitals.

Conclusion

The physical risk scoring of biomedical equipment can be changed from qualitative to quantitative using data from the FDA incident reports from 1984 to present. The traditional scoring system is not as accurate a predictor in the scoring of devices that can cause death or serious injury to patients. Changing the scoring protocol increases accuracy and can reduce the number of preventive maintenance procedures by 30 percent.

27

Part 3: An Analysis of Parts Replaced in a Medical Equipment Management Program

Introduction

Earlier papers (Collins and Dysko 2001; Collins 2004; Collins 2007) described a new risk assessment approach to preventive maintenance (PM) of medical equipment. By quantifying the physical and maintenance risk to patients posed by different classes of equipment, PMs could be narrowly tailored to the equipment that poses such risks, thereby eliminating at least 65 percent of the equipment from unnecessary PM within the nine-hospital system that was the subject of the five-year study.

Method

Earlier papers confined the analysis to equipment work histories established under an in-house clinical engineering department that succeeded the out-sourcing of PM procedures within the nine-hospital network. The five-year period provided a snapshot of repair histories for all equipment in place during the study, including any pieces retired or acquired during that time.

Presented here is more detail about the study protocol, beginning with a review of all work orders for those pieces of equipment or devices requiring


$50 or more in repairs: the repaired/replaced part(s) were entered on a spreadsheet with rows for manufacturer, model, and device. These entries comprised 335 models from 84 different devices that fit the requirement and a total of 1,824 parts replaced. The entire database contains 333 devices with 3,574 models. Reviewing work orders showed that it is necessary to read each one for accuracy and identify the actual part replaced. Often, technicians will replace circuit boards during a repair and will list those circuit boards as replaced, even where those replacements did not actually fix the problem and the original board was put back as described in the work order narration.

Results

Figure 3.1 shows the parts replaced, according to the part’s description. The most frequently replaced part is the circuit board, suggesting that circuit boards are responsible for the majority of equipment failures. The repair parts are classified by the type of device affected.

Figure 3.1 Number of Repairs by Part

Boards

Power s

upplyTu

be

Moto

r

Cable

Pump

Valve

Monito

r

Drive

Switc

h

Keyboar

d

Sensor

Colimat

or

Bucky

Tran

sducer

Amplifi

er

800

700

600

500

400

300

200

100

0

Total number of parts replaced by part type over the five-year history.

Figure 3.2 shows the devices that had circuit boards replaced. The number on the vertical axis is the total number of parts replaced divided by the total number of those devices in inventory, divided by five years. For instance, 96 boards were replaced in gamma cameras, and 27 cameras were in inventory. Five bone densitometers and six boards were replaced.


Figures 3.3 and 3.4 show the results for motors and power supplies replaced respectively, again with the number of replaced parts normalized by the total number of those devices repaired over five years.

Figure 3.2 Circuit Boards Repair/Device/Year

Analyz

er, chem

istry

Analyz

er, coag

ulatio

n

Analyz

er, hem

atolo

gy

Bone densit

omote

r

Camera

, gam

ma

Centrifu

ge

Inje

ctor,

angio

graphic

Lase

r im

ager

Linear

accele

rato

r

Monito

r, fe

tal

Monito

r, phys

iolo

gical

MRI

Pump (i

ntra-a

ortic b

allo

on)

Rad/F

luoro

, genera

l

Radio

graphic

C-A

rm

Radio

graphic

cat

h/vas

c

Radio

graphic

, chest

Radio

graphic

, cys

to

Radio

graphic

, genera

l

Radio

graphic

, mam

mogra

phic

Radio

graphic

, porta

ble

Scanner,

CT

Scanner,

ultras

onic

Steril

izer,

peracetic

acid

Ventilat

or

Number of circuit boards replaced for each device type divided by the total number of each device included in the five-year history.

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Figure 3.3 Motors Replaced/Device/Year

0.12

0.1

0.08

0.06

0.04

0.02

0

Motors replaced by device type normalized by the number of devices in the five-year history.

Analyz

er, chem

istry

Analyz

er, hem

atolo

gy

Analyz

er, im

munoas

say

Bone densit

omete

r

Camera

, gam

ma

Centrifu

ge

Centrifu

ge, refri

gerate

d

Equip., l

ab, w

asher

Film

load

er

Film

pro

cessor

Lase

r im

ager

Linear

accele

rato

r

Module

, NIB

PM

RI

Rad./F

luoro

., genera

l

Radio

graphic

, cat

h./vas

c.

Radio

graphic

, cys

to.

Radio

graphic

, genera

l

Radio

graphic

, mam

mogra

phic

Radio

graphic

, porta

ble

Scanner,

CT

Tread

mill

Ventilat

or

Was

her/Ste

rilize

r


The data was further analyzed to see which devices had any part replaced. Again this result has been normalized by the actual number of devices in each type of device.

Four devices in Figure 3.4 show significantly more replacement parts than the other devices in the figure. They are the gamma camera, radiographic unit cath/vasc, CT scanner, and pericetic acid sterilizer. Figures 3.6, 3.7,

Figure 3.4 Power Supply Replaced/Device/Year

Analyz

er, ele

ctrolyt

e

Analyz

er, urin

e

Anesthesia

unit

Bone densit

omete

r

Camera

, gam

ma

Camera

, lase

rECG

Film

pro

cessor

Lase

r im

ager

Monito

r, phys

iolo

gical

Pump, a

thro

mbic

Pump, in

tra a

ortic b

allo

on

Rad./F

luoro

., genera

l

Radio

graphic

, C-a

rm

Radio

graphic

, cat

h./vas

c.

Radio

graphic

, cys

to.

Radio

graphic

, genera

l

Radio

graphic

, mam

mogra

phic

Radio

graphic

, porta

ble

Scanner,

CT

Scanner,

ultras

onic

Ventilat

or

Was

her/Ste

rilize

r

Power supplies replaced by device type normalized by the number of the device in the five-year history.

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Figure 3.5 Parts Replaced/Device/Year

Analyz

er, hem

atolo

gy

Bone densit

omete

r

Camera

, gam

ma

Cath la

b dat

a m

gmt s

yste

m

Film

dig

itize

r

Lase

r im

ager

Linear

accele

rato

rM

RI

Nuclear

med. d

ata

mgm

t sys

tem

Pump, in

tra a

ortic b

allo

on

Rad./F

luoro

., genera

l

Radio

graphic

, C-a

rm

Radio

graphic

, cat

h./vas

c.

Radio

graphic

, chest

Radio

graphic

, cys

to.

Radio

graphic

, genera

l

Radio

graphic

, mam

mogra

phic

Radio

graphic

, porta

ble

Scanner,

CT

Steril

izer,

peracetic

acid

Was

her/Ste

rilize

r

3.5

3

2.5

2

1.5

1

0.5

0


3.8, and 3.9 show these four devices respectively with parts replaced for each specific device model for all of the devices, including those models that did not have any parts replaced.

Figure 3.6 Gamma Camera Repairs

BoardPMT

MonitorPower supply

Models

PartNumber

of Parts

Replaced

20

15

10

5

01 2 3 4 5 6 7 8 9

1011 12 13

1415

1617

1819

2021

2223

25

Type of part replaced for all models of the gamma camera.Note: PMT = photomultiplier tube

Figure 3.7 Radiographic, Cath/Vascular

PCBMonitor

SwitchDrive

Power supplyTube

Models

Part

Number

of Parts

Replaced

1 2 3 4 5 6 7 8 910 11 12 13

14 15

40

30

20

10

0

Type of part replaced for all models.


Figure 3.8 CT Scanner Repairs

PCB

Motor

Power supply

Tube

Models Part

Number

of Parts

Replaced

1

3

5

7

9

11

13

20

15

10

5

0


Figure 3.9 Device Repairs by Hospital, Arranged by Bed Size

The three device repair statistics in figures 3.6, 3.7, and 3.8, showing the hospitals arranged by bed size with the largest hospital at the back.

Camera radiography

Gamma cath/vasc

Scanner, CT

H8

H7

H6

H5

H4

H3

H2

H1

40

30

20

10

0


In Figure 3.11, the same data is displayed but is only for devices that had more than 10 circuit board replacements, which gives a better picture of which devices had extensive repairs over the five year period.

Figure 3.10 Pericetic Acid Sterilizer


Pump

Power supply

MotorPower supply

Board

Board1

2

20

15

10

5

0

Models

Part

Number

of Parts

Replaced

Figure 3.11 Parts Replace (>10 Boards)

Device part replacements for all repairs where more than 10 circuit boards per device were replaced.

Camera, gammaLinear acceleratorRad./Fluoro., general

Radiographic, C-arm

Radiographic, cath./vasc.

Radiographic, cysto.

Radiographic, general

Radiographic, mammographic

Radiographic, portable

Scanner, CT

Scanner, ultrasonic

100

50

0

Device

Number

of Parts

Replaced


Discussion

The study’s most important finding is that circuit board failure is the most frequent reason for repairs of medical equipment and devices. Most of the other parts needing replacement are predictable; their eventual failure is due to the wear and tear of mechanical motion that occur even with regular PM.

Since most medical equipment in use today has evolved from the analog to the digital world, we would expect to see circuit board failures in a large group of devices. The majority of board replacements occur in imaging devices by a five to one margin, while most other devices need less than one board replacement per year. Given the heavy use that imaging equipment undergoes, these replacements should not come as a surprise. Looking at all other device types in Figure 3.2, we see that although board replacement is the biggest category, it still equates to fewer than than two boards replaced in a five-year period.

Figures 3.3 and 3.4 show similar results for replacement of motors and power supplies. Although these replacements are predictable, they still did not occur with great frequency in the five-year period. For example, the replacement rate in centrifuges is 21 motors replaced in a total of 92 centrifuges.

Figure 3.4 shows device types with total number of parts replaced in each of the five years. We can see that the major devices requiring the greatest number of replacements are the gamma camera, the radiological unit used in catheterization labs, the CT scanner, and the pericetic acid sterilizer. The first three would be expected; they are in constant use for long periods of time on a daily basis. The pericetic acid sterilizer parts replaced were primarily (about 80 percent of the replacement total) valves and pumps.

Another variable, seen in Figure 3.9, shows that the largest number of repairs occurs in the larger hospitals. As hospital bed size decreases, the number of repairs for the same type of device decreases.

Applicability of this Approach in other Settings

Given that most biomedical/clinical engineering departments have had computerized maintenance programs in place for a number of years, it is recommended that hospitals perform their own analysis. Once baseline information is in place, which will take the most amount of time, tracking


repairs on a monthly basis requires a minimal amount of time due to the paucity of relevant repairs.

Results will depend on the size of the hospital and equipment usage. This study shows a wide range in results for different devices since the hospitals involved ranged from about 100 beds to over 650 beds. Four of the hospitals have over 300 beds.

Conclusion

Repair work orders for all parts costing greater than $50 were analyzed over a five-year period for the nine hospitals in a network. The $50 figure was arbitrarily chosen to decrease the number of work orders printed for analysis and could probably be increased without losing any important part replace-ment instances. The hospitals range in size from 100 to 650 beds, with four hospitals having more than 300 beds, and are located in urban, exurban, and suburban locations. The work order report form was modified so that about four orders were printed on a single page containing the pertinent informa-tion for analysis, reducing the amount of paper required. The initial analysis was for the first two years of work histories and from then on, monthly reports were obtained. The average number of pertinent work orders for each hospital on a monthly basis was about four. The results show that when the data is analyzed and normalized to a yearly interval and by the number of devices, the number of major parts replaced in medical equipment is rather small. The largest category, circuit boards, are replaced across the spectrum of device types, but in the majority of cases the replacements occur about once in five years.

This analysis should be done to obtain data that could be used in a medi-cal equipment risk assessment program instead of the qualitative guesswork method now commonly used.

Although this approach requires an initial commitment of many hours to develop a baseline, once established, it is relatively simple to maintain an active repair history database. The number of relevant repairs required per quarter is fairly small and manageable. This quantitative approach is a more efficient use of medical personnel and financial resources, establishing a PM program founded on measurable, quantifiable data to ensure reduction of patient risk.

37

Appendix A

List of Device Types with R > 0.5

Description R Mfg. Models Description R Mfg. Models

Radiographic, cath./vasc/ 12.75 4 13 Cytometer, flow 1.29 1 1

Rad./Fluoro., general 10.68 8 29 Dialysis unit 1.22 2 7

Laser, excimer 10.56 2 2 Analyzer, electrolyte 1.22 2 5

Film loader 9.51 1 2 Camera, laser 1.21 4 9

Nuc. med. data mgmt. system

8.63 1 2 Analyzer, tissue 1.16 1 2

Laser imager 8.14 4 7 Ultrasound data mgmt. system

1.15 2 2

Film processor, digital 7.99 3 7 MRI 1.13 3 6

Radiographic, chest 7.71 3 3 EOG data mgmt. system

1.05 3 10

Camera, gamma 7.18 8 23 Equip., lab, washer 1.05 4 4

Analyzer, hematology 6.95 2 14 Analyzer, microbiology

0.96 3 4

Washer/Sterilizer 6.91 1 5 Injector, angiographic 0.96 4 25

Microscope, electron 6.88 2 2 MRI, open 0.96 2 2

Linear accelerator 6.14 3 7 Radiotherapy simulator

0.92 3 3

Scanner, CT 5.44 5 13 Laser, diode 0.88 1 1

Sterilizer, pericetic acid 4.93 2 2 Analyzer, blood gas 0.8 6 15

Radiographic, C-arm 4.6 3 5 Radiographic, portable 0.75 3 13

Sterilizer, steam 4.48 6 30 ENG 0.71 1 2


Analyzer, urine 4.13 3 8 Tissue processor 0.71 5 9

Analyzer, chemistry 3.97 10 19 Ventilator 0.71 14 34

Bone densitometer 3.78 2 3 Phacoemulsifier 0.68 4 8

Cath lab data mgmt. system 3.58 3 3 Monitor, metabolic 0.67 1 1

Film processor 3.39 10 35 Laser, holmium/YAG 0.66 2 2

Sterilizer, ETO 2.92 2 4 Scanner, ultrasonic 0.66 18 44

Radiographic, cysto. 2.68 5 7 Monitor, physio., cath lab

0.64 4 4

Radiographic, general 2.55 14 43 Urodynamic data mgmt. system

0.64 7 9

Plasma thawer 2.31 1 1 Warmer, radiant 0.64 7 22

Telemedicine system 2.25 1 1 Gastroscope 0.63 2 23

OB data mgmt. system 2.11 2 2 Laser, YAG 0.63 6 6

Radiographic, dental 2 1 2 Xenon delivery system 0.63 3 4

Radiographic, mammographic

1.99 8 19 Apheresis unit 0.59 1 1

Timer, coagulation 1.88 2 2 Incubator, infant, transport

0.57 4 9

Camera, ophthalmic 1.53 2 2 Tissue embedding system

0.57 4 7

Work station 1.43 10 14 Concentrator, 02

0.53 5 10

Nurse call system 1.41 7 30 Monitor, physio., stress

0.52 7 14

Pump, intra-aortic balloon 1.3 1 4 Defibrilator 0.5 7 34

Centrifuge, refrigerated 1.29 6 8

Description R Mfg. Models Description R Mfg. Models

39

References

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