Evaluation of the awareness of electromagnetic compatibility and interference for improved medical device management observed in selected Rwanda district hospitals

Abstract

Accuracy and reliability of medical device measurements are essential for high-quality healthcare delivery. Electromagnetic compatibility (EMC) is 1 of the conditions that can be monitored when managing electronic and wireless medical devices in hospitals. Noncompliance with EMC can lead to electromagnetic interference (EMI), risking catastrophic failures, especially in vital sign devices such as defibrillators and pacemakers, and potentially irreversible harm to patients’ health. This study assessed the knowledge of biomedical engineers and technicians regarding EMC and EMI in hospital devices and their effects. It also examines how hospitals manage biomedical equipment and the frequency of wireless communication usage. This study utilized an anonymous questionnaire administered to 35 biomedical personnel in Rwanda district hospitals. Face-to-face interviews were used for data collection. SPSS was used to analyze the data through descriptive analyses statistically, and a regression model was designed to evaluate the impact of electromagnetic compatibility and interference on medical device management (MDM). A statistical significance of 0.05 was assumed. The regression model predicted MDM with a significance level of 0.009 based on ANOVA values. A Pearson’s correlation test was also performed for the model. The results indicated an inverse relationship of −0.470 for EMI against MDM and −0.469 for EMC against MDM. A total of 24 (68.6%) respondents were unaware of electromagnetic compatibility/interference issues and 11 (31.4%) were familiar with it. All 35 respondents indicated that maintenance is currently being performed on medical devices. High usage of wireless devices, for professional and personal purposes is highlighted. Based on the results, both electromagnetic compatibility and understanding of interference influenced MDM. To ensure optimal medical device performance and improve hospital safety, we recommend continuous monitoring of medical device electromagnetic compatibility. Additionally, EMC/EMI awareness training can be incorporated into on-the-job training programs for biomedical staff members.

1. Introduction

The increasing prevalence of technology is prominently integrating wireless features into medical and nonmedical devices, thus populating hospitals with electrical and wireless devices for work and personal use, which should effectively cohabit. The coexistence of electronic medical devices with other wireless devices requires enhanced electromagnetic compatibility (EMC) management. EMC management involves controlling electromagnetic interference (EMI) and reducing susceptibility.^[1] The possibility of EMI in medical devices such as ECGs, pacemakers, and defibrillators from wireless medical devices, mobile phones, and Wi-Fi has been explored, in hospital settings.^[2] Numerous reports have been published on malfunctions caused by interference.^[3,4] Hundreds of reports of medical device malfunctioning caused by EMI are made to the Food and Drug Authority (FDA).^[5] The MAUDE database has 16,304 reported cases based on EMI, EMC problems, or radio frequency (RF)-related failures.^[6] To address these concerns, manufacturers are producing medical devices that comply with the revised regulations set to consider medical device EMC, considering the latest technologies.^[7] Standards and directives have been established to govern device compliance and ensure product safety.^[8,9] Nevertheless, a knowledge gap persists among biomedical personnel regarding the EMC/EMI aspects of device operation and management. This complicates the evaluation of whether hospitals effectively manage medical device EMC. Management-related problems pose risks of device malfunction, potentially causing misdiagnosis, harm to operators, or even complete device failure.^[7]

Effective EMC/EMI management is achieved when hospital personnel and management are aware of the associated dangers. EMI management includes educating device operators, identifying potential EMI sources in the hospital, and testing the radiation fields between medical devices and other electronic devices in the vicinity.^[10] Geoffrey et al evaluated medical students’ understanding of nonionizing radiation types and associated hazards and revealed insufficient knowledge to ensure safety from EMI.^[11] Hours et al investigated the impact of electromagnetic fields on actively implanted devices in France, and several EMI occurrences were noted. Underreported incidents hindered effective follow-ups with healthcare authorities. Although with a low probability, the risk of interference between EMI sources and active implanted devices is real and necessitates vigilance management.^[12] Gutierrez et al surveyed hospital personnel’s knowledge of EMC in medical devices and measured magnetic fields to confirm its existence in hospitals. A lack of knowledge about EMC was exhibited among the staff and the electromagnetic field levels measured were above limits.^[13] Most of the literature is from developed countries; medical device EMC/EMI and management in African hospitals are understudied.

This study aims to bridge this gap by assessing EMC and EMI awareness of biomedical engineers and technicians, evaluating MDM, assessing wireless usage, exploring technical details of the devices in Rwandan hospitals, and the impact of EMC and EMI on MDM. This study establishes a baseline for developing an IoT-based EMC monitoring system in developing countries to, ensure device longevity and compliance.

2. Methodology

The study was based on a cross-sectional design, in which survey questionnaires were administered through face-to-face interviews with biomedical engineers and technicians working in Rwanda district hospitals. Hospitals were the focus of this study, as they use a large quantity of medical equipment. According to the Rwanda Medical and Dental Council, Rwanda has 42 district hospitals.^[14,15] A sample of 35 hospitals was used to conduct this survey. District hospitals were sampled for their medium size, diverse medical equipment availability, significant patient service, and the likelihood of employing full-time biomedical staff compared with local clinics. All 5 provinces of Rwanda were included in the survey. Biomedical engineers and technicians were targeted because they are medical equipment managers. This study created a representation of the awareness levels of EMC/EMI, its occurrence, and the current status of medical equipment conditions and management. It also explored the relationship between EMC, EMI, and MDM. Thus, offering regulatory authorities and hospital management clear insights for advancing the knowledge and EMC management of medical devices in hospitals. It allows management to understand the relationship between wireless technologies, MDM practices, and the importance of EMC/EMI awareness among the medical staff.

The research roadmap, depicted in Figure 1 entails questionnaire development, ethical clearance, hospital approval, verbal consent from participants, interviews, data capturing, and the SPSS software tool used to analyze the data. A questionnaire was developed and assessed by the research team and an ethical clearance was obtained from the University of Rwanda’s Institutional Review Board. Before data collection, a support letter from the University of Rwanda Centre of Excellence in Biomedical Engineering and e-Health (UR-CEBE) and ethical clearance were submitted to the hospitals, with a reception stamp obtained from each. Verbal consent was obtained from the participants before the interview to ensure their willingness to participate. The survey was conducted over 9 months, from October 2022 to June 2023. After each interview, data were captured and stored on Google Sheets, and synthesis, cleaning, and analysis were performed.

Figure 1.

Research Roadmap describes the steps and activities taken to conduct the current study.

2.1. Data collection

A structured questionnaire was used as the research instrument to explore EMC/EMI knowledge, assess wireless device prevalence, evaluate equipment management, and investigate biomedical device technical details. It comprises 4 categories of data: personal information, wireless usage information, equipment management practices, and knowledge of EMC/EMI information. Personal information included age, sex, occupation, length of hospital tenure, wireless devices used for personal purposes, wireless devices used for work purposes, and patients’ use of wireless devices in the hospital. Awareness of EMC/EMI included knowledge of electromagnetic compatibility, frequency of medical equipment maintenance, equipment rollout periods, satisfaction with medical equipment performance, knowledge of medical device interference, witness to EMI occurrence, and damage or failure of equipment due to interference, damages, or failures of medical equipment where the causes were unidentified. The participants were asked about the value of monitoring electromagnetic radiation around the equipment and its potential to enhance patient and staff safety. Each participant was asked 31 questions in total.

The questionnaire design was based on the premise that EMI occurs when there is an emitter, transmitter, and susceptible device within the range of each other. To ensure that medical devices are EMC-compliant throughout their lifespan, it is necessary to pay attention to the devices’ immunity, knowledge of the expert managing the equipment, and the availability of potential sources of EMI in the environment. Pre-compliance testing is insufficient because there are factors that directly affect the electromagnetic characteristics of the devices. Device aging, corrosion, faults, wear and tear, and misuse can cause mechanical stresses that increase the likelihood of experiencing EMI.^[16]

2.2. Design of a regression model

Regression is a statistical analysis that identifies the associations between variables in each dataset. To evaluate the hypotheses, factors describing EMC, EMI, and MDM were categorized based on the results of the survey data collected. The linear regression model design for the prediction of MDM is shown in Figure 2. Medical device management (MDM) is a combination of ECG device usage, average condition, training for device usage, operating voltage, operating frequency, Internet connection, age, maintenance, and disposal. In our scenario, EMC comprised EMC knowledge, failures from EMI, and safety levels. EMI was characterized by EMI knowledge, witnessing the occurrence of EMI, damage to medical devices from EMI, and failures of medical devices from unidentified sources.

Figure 2.

Model design depicts the hypotheses which can be broken down into $H_1$ the influence of EMC on MDM and $H_2$ the influence of EMI on MDM. EMC = electromagnetic compatibility, EMI = electromagnetic interference, MDM = medical device management.

$H_{01}$: EMC knowledge does not influence MDM.

$H_{A1}$: Knowledge of EMC has a positive effect on MDM.

$H_{02}$: Knowledge of EMI does not influence MDM.

$H_{A2}$: Knowledge of EMI has a positive effect on MDM.

$${\displaystyle \begin{array}{l}{\displaystyle Y=B_0+B_1+B_2+E}\end{array}}$$ (1)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{M}\mathrm{D}\mathrm{M}=B_0+B_{\mathrm{E}\mathrm{M}\mathrm{C}}+B_{\mathrm{E}\mathrm{M}\mathrm{I}}+E}\end{array}}$$ (2)

Where:

MDM: Medical Device Management.

EMC: electromagnetic compatibility.

EMI: electromagnetic interference.

E: error margin.

A linear regression analysis was performed to determine whether EMC and EMI levels predicted the effectiveness of MDM. Evaluations based on coefficients, ANOVA, and Pearson correlation tests were used in the regression model.

3. Results

Thirty-five responses were obtained from questionnaires administered through face-to-face interviews, denoting a 100% response rate. Responses were manually entered into a data sheet for storage and analysis. The data were analyzed using IBM SPSS Statistics 21 software. Of the 35 respondents, 23 disclosed their age. The youngest participant was 27 years old, whereas the oldest was 53 years old. The mean age was 40.4 ± 9.77 years [median, 40 years; interquartile range of (31, 49) years]. A total of 30 (85.7%) of the participants were biomedical technicians and 4 (11.4%) were biomedical engineers. Eight had worked at their respective hospitals for 1 to 2 years, 5 were in the 3 to 5 years range, 8 were in the 6 to 10 years range, 10 in the 11 to 15 years, and 4 had been there for more than 16 years. The mean and standard deviation of job experience were 2.91 and 1.36, respectively.

3.1. On the knowledge of EMC/EMI

Knowledge of EMC/EMI impacts how a biomedical engineer or technician manages medical devices considering a device’s electromagnetic compatibility fulfillment. Of the 35 respondents, 24 (68.6%) were unaware of E and 11 (31.4%) were knowledgeable. Regarding electromagnetic compatibility, 11 (31.4 %) knew about it whereas 24 (68.6%) did not. Among respondents, 20 (60%) were unsure if they had ever witnessed device interference, 7 (20%) stated that they had not witnessed it, and 7 (20%) admitted to witnessing it. Exactly 23 (65.7%) spelled out that they did not know whether there was equipment in their hospitals that was damaged or failed due to interference, 4 (11.4%) agreed to have equipment that failed due to interference, and 8 (22.8%) stated that they had not observed equipment failure due to interference. Nineteen (54.3%) of the technicians experienced unidentified equipment failures, 13 (37.1%) did not, and 3 (8.6%) were uncertain. Twenty-one thought device monitoring could improve safety by 91% and above, 7 in a 76% to 90% improvement, 5 expected a 50% to 75% increase, and 1 each believed in a 26% to 50% and 11% to 25% improvement. The EMC/EMI results are listed in Table 1.

Table 1.

Knowledge of EMC/EMI in medical devices.

Variable	Option	Responses	Percent	Mean	STD deviation
EMI knowledge	Yes	11	29.6%	1.69	0.222
	No	24	68.6%
EMC knowledge	Yes	11	29.6%	1.69	0.222
	No	24	68.6%
EMI occurrence witnessing	Yes	7	20%	2.4	0.812
	No	7	20%
	I do not know	21	60%
Damage/failures of medical devices due to interference	Yes	4	11.4%	2.53	0.706
	I do not know	23	65.7%
	No	8	22.8%
Failures of medical devices from unidentified causes	Yes	19	54.3%	1.54	0.657
	No	13	37.1%
	I do not know	3	8.6%
Percentage increase in overall safety from device monitoring	11% to 25%	1	2.9%	5.31	1.022
	26% to 50%	1	2.9%
	5% to 75%	5	14.3%
	76% to 90%	7	20%
	91+%	22	60%

Abbreviations: EMC = electromagnetic compatibility, EMI = electromagnetic interference, STD = standard.

3.2. Evaluating wireless usage in hospitals

Of the 35 respondents, 31 (88.6%) indicated major devices wirelessly enabled at their hospitals, and 4 (11.4%) indicated that they did not. All respondents confirmed 100% wireless capability for imaging devices, 60% for laboratory equipment, and 25.7% for physiological monitors. All 35 respondents indicated that they used wireless devices for work purposes. Laptops/notebooks were used (100%), mobile phones (97%), printers (80%), smart wearables (23%), tablets (20%), medical devices (14%), and walkie talkies (3%) for work purposes. All 35 respondents indicated that they used wireless devices at the hospital for personal purposes. Laptops/notebooks are used 82.9%%, mobile phones 88.6%, smart wearables and tablets 11.4%, printers 5.7%, and medical devices 3%, for personal purposes. All respondents indicated that the patients and visitors used cell phones or other wireless mobile devices in the hospital. Mobile phones are used (88.9%), laptops or notebooks (74.3%), smart wearables (22.7%), and tablets and medical devices (20%). The results are shown in Figure 3.

Figure 3.

Use of wireless devices in hospitals is a representation of the distribution of the usage of wireless devices in hospitals classified into 3 categories; work purposes, personal purposes, and patient use.

3.3. Equipment management practices

Regarding management practices, researchers asked questions on the assessment of medical equipment management practices, including their introduction, equipment rollout, disposal, age of the oldest equipment in the hospital, equipment maintenance, and frequency. All the respondents (100%) indicated that maintenance was carried out on medical equipment in the hospitals. Various equipment acquisition methods were employed in each hospital. Equipment was predominantly acquired through donations (91.4%), purchases (91.7%), rentals (54.3%), and leases (11.4%). The maintenance of medical devices was mostly performed 2 to 3 times per year (61.5%) 4 to 6 times per year (40%), once per year (2.8%), and monthly (5.7%). Medical equipment was disposed of due to failure (51.4%), replacement with new equipment (20%), end of life (25.7%), and 2.9% were unsure of the disposal criteria. Respondents cited the age distribution of hospital equipment: 14.3% aged 0 to 5 years, 37.1% aged 6 to 10 years, 28.6% aged 11 to 20 years, 17.1% aged 20 + years, and 2.85% did not provide age information. The results of equipment management are presented in Table 2.

Table 2.

Medical equipment management.

Variable	Option	Responses	Percent	Mean	STD deviation
Medical device acquisition	Donated	32	91.4%	1.09	0.284
	Bought	34	97.1%	1.09	0.284
	Rented	19	54.3%	1.46	0.505
	Leased	4	11.4%	1.89	0.323
Frequency of medical device maintenance	Once a year	1	2.8%	1.77	0.973
	2 to 3 times per yr	18	51.4%
	4 to 6 times per yr	14	40%
	Every month	2	5.7%
Equipment disposal/change/rollout	When equipment fails	18	51.4%	4.17	1.014
	When there is equipment replacement	7	20%
	When it reaches the end of life	9	25.7%
	I do not know	1	2.9%
Age of the oldest equipment	0 to 5 yr	5	14.3%	2.5	0.916
	6 to 10 yr	13	37.1%
	11 to 20 yr	10	28.6%
	20 + yr	6	17.%
	Not indicated	1	2.8%

STD = standard.

We surveyed ECG machines because they are susceptible to interference.^[17] Table 3 provides a representation of the technical details of ECG machines. Of the 35 respondents, 80% indicated the use of ECG machines, whereas 20% did not. From the population that uses ECG machines, 60% of the machines are brand new, 21% are above average, 25% are average, and 11% are below average in condition. The staff were also trained to use the equipment, indicated by 68% and 32% who were not trained. The operating frequency of the ECG machines was 50 to 60 Hz with an operating voltage of 220/230 V. Only 16% of the ECG machines require Internet connectivity, and the remaining 84% do not use the Internet. The ages of the ECG machines in the surveyed hospitals were distributed as follows: 26.7% were 0 to 3 years old, 43.3% were 3 to 7 years old, 23.3% were 7 to 10 years old, and 6.7% were 10 to 15 years old.

Table 3.

Technical details for ECG machines.

Variable	Option	Responses	Percent	Mean	STD deviation
ECG/EKG usage	Yes	28	80%	1.2	0.4058
	No	7	20%
ECG/EKG condition	Brand new	17	60%	2.1429	1.2866
	Above average	6	21%
	Average	7	25%
	Below average	3	11%
ECG/EKG usage training	Yes	20	57.1%	1.4286	0.5021
	No	9	25.7%
	Not indicated	6	17.1%
ECG/EKG operating voltage	220 to 230 V	26	100%	1.1429	0.42997
ECG/EKG operating frequency	50 to 60 Hz	26	100%	1.0	0
ECG/EKG internet connectivity	Yes	4	11.4%	1.7143	0.4584
	No	24	68.6%
	Not indicated	6	17.1%
ECG/EKG age	0 to 3 yr	8	26.7%	2.1429	0.8096
	3 to 7 yr	13	43.3%
	7 to 10 yr	7	23.3%
	10 to 15 yr	2	6.7%

ECG/EKG = electrocardiogram, STD = standard.

3.4. Model evaluation

A Linear Regression analysis was performed on the survey data collected from biomedical engineers and technicians, and the results are shown in the coefficient table, ANOVA, and Pearson correlation tests. The model was implemented using a sample of 35 biomedical engineers and technicians. According to the coefficient table (see Table 4), knowledge of EMI has a significance of 0.191 which is >0.05. The null hypothesis is not rejected; thus, it has no significant impact on MDM. Knowledge of EMC has a significance of 0.204, which is >0.05; hence, we cannot reject the null hypothesis. Therefore, it does not significantly influence MDM. The regression model predicts MDM significantly well because it has an ANOVA(F) value of 5.466, which reaches significance by a P-value of .009, which is below the .05 level of significance. Therefore, there is statistical significance between the means of EMC and EMI, and the null hypothesis is rejected. The ANOVA results are listed in Table 5.

Table 4.

Coefficients table.

Coefficients*
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.
		B	STD. Error	Beta
1	(Constant)	24.235	2.752		8.807	0.000
	EMI	−.422	.315	−.283	−1.338	0.191
	EMC	−.566	.436	−.274	−1.298	0.204

^*Dependent variable: MDM.

Abbreviations: EMC = electromagnetic compatibility, EMI = electromagnetic interference, MDM = medical device management.

Table 5.

ANOVA results.

ANOVA*
Model		Sum of squares	DF	Mean square	F	Sig.
1	Regression	62.823	2	31.411	5.466	.009†
	Residual	178.148	31	5.747
	Total	240.971	33

^*Dependent variable: MDM.

^†Predictors: (Constant), EMC, EMI.

Abbreviations: ANOVA = analysis of variance, EMC = electromagnetic compatibility, EMI = electromagnetic interference, MDM = medical device management.

3.5. Pearson correlations

The Pearson correlation test results are presented in Table 6. EMI has a negative correlation of −0.470 for MDM, while EMC has a correlation of −0.469 for MDM.

Table 6.

Pearson correlations table.

Correlations
	MDM	EMI	EMC
MDM	1
EMI	−.470*	1
EMC	−.469*	.682*	1

^*Correlation is significant at the 0.01 level (2-tailed).

Abbreviations: EMC = electromagnetic compatibility, EMI = electromagnetic interference, MDM = medical device management.

4. Discussion

Participants in the interviews included 85.7% technicians and 11.4% engineers. This reflects the youthfulness of the Biomedical Engineering field in Rwanda, resulting in fewer technicians transitioning to becoming engineers.^[18] Half of the respondents had spent more than 10 years in the biomedical technician field. As such, we recommend the inclusion of EMC topics in the periodic training sessions to keep them abreast of the rapidly evolving medical technologies. Most of the participants (68.6%) did not know about electromagnetic compatibility and interference, which in turn could lead to inadequate management of such issues. Considering this, there is a need for a curriculum review to include EMC management concepts.^[11] A total of 23 biomedical technicians were unaware of whether any damage or failure to medical devices resulted from inference. According to Gutierrez et al, many failures can occur owing to interference without anyone noticing.^[13]

Heavy usage of wireless devices, such as mobile phones and laptops, by staff, patients, and visitors in hospitals poses a risk of increased EMI levels. Care must be exercised to reduce potential medical device interference. Technological advances are increasing the prevalence of wireless and electronic devices in hospitals, thereby increasing the density of medical and wireless equipment. As the radio spectrum becomes more congested, the likelihood of interference increases. With Wi-Fi, Bluetooth, RFID, and ZigBee technologies sharing the Industrial Scientific and Medical bands, coexistence is crucial for medical, communication, and IT devices to function as intended. The use of certified devices and consideration of EMI risk upon the introduction of new equipment and continuous operation in healthcare facilities are ways to mitigate EMI.^[7] Based on the results, 91.4% of the medical devices were donated, 97.1% were bought, 54.3% were rented and 11.4% were leased. In Rwanda, donations are vital for improving hospital capacity and healthcare in this developing nation. However, managing donated devices requires extra care because of the lack of history regarding their working conditions. This is evident in the prevalence of devices aged 10 to 20 and 20 + years in hospitals. Older devices struggle with limited compatibility with newer wireless technologies introduced in hospitals, for work or personal use.^[19] Recent devices face lower interference risks because manufacturers comply with updated standards such as IEC 60601-1-2, 4th edition, which mandates a premarket risk analysis for device performance and safety.^[20] Although Rwanda mandates the control of donated medical devices, regulations on medical equipment in developing countries are lightly enforced.^[21,22] The existence of medical devices older than 20 years underscores the ongoing challenges faced by developing nations in adequately equipping their healthcare infrastructure. This highlights the need for additional training and resources in these regions.

Equipment was disposed of due to failure (51.4%), 25.7%, when it reached the end of life, 20% when the hospital was replaced, and 2.95%, were unaware when the equipment was disposed of. This can be attributed to the inadequate regulation of medical devices and a lack of awareness regarding the potential repercussions of prolonged device utilization beyond the intended operational lifespan. These findings align with the literature in that most medical devices in developing countries are donated, and that there is a need to improve device regulation.^[23] It also indicates the need to raise awareness among biomedical engineering personnel who are central to hospital equipment management, aligning with the existing literature.

Eighty percent of hospitals use ECG machines, because of their essential role in treating cardiovascular diseases.^[24] Following the responses obtained, the ECG machines available in district hospitals were 60% brand new, 21% above average, 25% average, and 11% below average in condition. Equipment that is below average in condition may be attributed to the acquisition cost of new equipment, which is an uphill task for developing nations. Training to use the equipment was performed in 57.1% of the hospitals, whereas 25.7% were not trained. This outcome may be because the research targeted biomedical engineers and technicians who are not direct operators of the ECG machines. Only 16% of the respondents indicated the need for internet connectivity to use the ECG machines while 84% did not. The 16% of responses indicated ECG machines need Internet connectivity, which may be attributed to the new machines that are Internet-enabled, whereas the majority may represent older machines and some biomedical engineers/technicians unaware that the ECG machines are Internet-enabled.^[24] The 16% requiring internet could be newly acquired ECG machines. The highest number (43.3%) of ECG machines was 3 to 7 years old, 26.7% were 0 to 3 years old, 23.3% were 7 to 10 years old and 6.7% were 10 to 15 years old. Most medical devices have a lifespan of 5 to 15 years hence, ECG machines are within acceptable age ranges.^[25]

A linear regression model was implemented on 35 biomedical engineers and technicians to determine whether EMC and EMI predict MDM. The coefficient results indicated a significant positive relationship between the independent and dependent variables. According to the ANOVA, the model predicts MDM significantly by an F (5.466) and a significance value of 0.009, suggesting that EMC and EMI have a meaningful impact on the variation in MDM levels. The model also showed a moderate negative Pearson correlation of −0.470 for EMI against MDM and −0.469 for EMC concerning MDM. This can be interpreted as effective MDM is attained when there are lower levels of EMI and EMC issues and vice versa. This indicates an inverse relationship, with a fair degree of consistency. Although medical equipment is resilient to external interference, biomedical technicians must remain vigilant in identifying and addressing issues that ensure accurate and reliable performance. It is recommended that healthcare facilities are recommended to assess, manage, coordinate, educate, establish, and implement written policies and report EMI problems to reduce EMI cases.^[26] The study helps to raise awareness among stakeholders towards equipping technicians and engineers with adequate training for optimal equipment performance.

5. Conclusion

To assess the level of EMC/EMI awareness among biomedical staff, device management practices, and wireless communication prevalence, a cross-sectional survey was administered to 35 Rwanda district hospitals. The respondents included 85.7% technicians and 11.4% engineers. The majority (68.6%) were unaware of the aspects of EMC and EMI, with 31.4% being aware of such issues and topics. There is widespread wireless usage in hospitals for work and personal purposes, which can pose a risk of interference. Medical equipment is frequently maintained, with 51.4% maintaining 2 to 3 times per year and 40%, 4 to 6 times per year; thus, we conclude that equipment is well maintained. Knowledge of EMC/EMI is present in less than half of the hospitals, which may pose a risk to the reliability of equipment in them. Hence, we recommend raising awareness of EMC/EMI among all hospital staff members. This knowledge enhances the quality of the device management services that they provide. The inclusion of EMC/EMI in the curriculum, on-the-job periodic training, and workshops can improve device management to ensure safer cohabiting of healthcare facilities and may be beneficial to the healthcare industry. A linear regression model was developed to determine the influence of EMC and EMI on MDM and the results indicated a significant impact based on ANOVA with F = 5.466 and a significance of 0.009. The management of EMC and EMI influences overall MDM. This study assessed the level of EMC and EMI awareness among biomedical staff. The results represent biomedical engineers’ and technicians’ perspectives, which can help raise awareness among hospital management. Future research will monitor the amount of radiation around medical devices to ensure that electromagnetic compatibility is satisfied throughout the life of a device.

Acknowledgments

This work was supported by a PASET- RSIF Research Grant and a Fulbright Foundation Research Grant.

Author contributions

Conceptualization: Chiedza Hwata, Omar Gatera, Gerard Rushingabigwi, Bolaji Thomas.

Data curation: Chiedza Hwata, Celestin Twizere, Nicola Ritsch, Therese Uwiragiye.

Formal analysis: Daniel Nsengiyera.

Funding acquisition: Omar Gatera.

Investigation: Chiedza Hwata, Nicola Ritsch, Therese Uwiragiye.

Methodology: Chiedza Hwata, Gerard Rushingabigwi, Celestin Twizere, Didacienne Mukalinyigira.

Project administration: Omar Gatera.

Software: Daniel Nsengiyera.

Supervision: Gerard Rushingabigwi, Celestin Twizere, Didacienne Mukalinyigira, Bolaji Thomas.

Validation: Celestin Twizere, Didacienne Mukalinyigira, Bolaji Thomas.

Visualization: Chiedza Hwata, Daniel Nsengiyera.

Writing – original draft: Chiedza Hwata.

Writing – review & editing: Omar Gatera, Gerard Rushingabigwi, Celestin Twizere, Didacienne Mukalinyigira, Bolaji Thomas, Nicola Ritsch, Therese Uwiragiye.