Bridging the Digital Divide: A Multi‐Method Evaluation of Nursing Readiness for Digital Health Technology

ABSTRACT

Aim

The aim of this study was to explore the digital health technology readiness of nurses, nursing students, nurse‐academics, and nurses in leadership roles. Workforce digital readiness impacts the adoption of digital health technologies and quality and safety outcomes. This study sought to identify key factors affecting nurses' readiness for specific digital health technologies and provide recommendations to accelerate readiness levels in alignment with rapidly advancing digital health technologies.

Design

Cross‐sectional multi‐method study.

Methods

An online survey was followed by semi‐structured interviews. Survey data (N = 160) were analysed using descriptive and inferential statistics, whereas qualitative responses (N = 8 interviews, 43 open‐ended responses) were thematically analysed.

Results

Participants were confident regarding openness to innovation, reporting highest confidence Levels around telehealth, wearable devices, and information technology. The lowest confidence scores were seen in health smart homes technology, followed by health applications, social media, patient online resources, and EHRs. Four themes were developed from the qualitative interviews including ‘opportunities for efficient ways of working’, ‘digital technology turning experts into novices’, ‘disillusionment between expectation and reality’ and ‘shared responsibility for development of digital expertise’. Open‐ended data was focused on the need for comprehensive education, ongoing support, and infrastructure improvements to prepare healthcare professionals for digital health environments.

Conclusions

Notable findings include age‐related differences, the need for shared responsibility in workforce preparation, and a link between problem‐solving ability and help‐seeking.

Implications for the Profession and/or Patient Care

Low confidence among nurses around the use of digital health technologies such as electronic health records, in‐home monitoring technology, and other wearable technologies could impact adoption readiness. Because patient safety is increasingly and inextricably linked to digital health technologies, nurses must not only be digital health literate but also included in the design and implementation process of these technologies.

Reporting Method

This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for the reporting of cross‐sectional survey research, and the Consolidated Criteria for Reporting Qualitative (COREQ) research guidelines.

Patient or Public Contribution

Limited patient and public involvement was incorporated, focusing on feedback from digital health researchers and practitioner‐academics during the academic peer review process. Their insights informed the clarity and relevance of the survey design and data interpretation, ensuring alignment with real‐world workforce development priorities in nursing.

Summary.

• What problem did the study address?

  • ○
    This study provides empirical evidence of variation in nurses' digital confidence across specific digital health technology domains including telehealth, wearables, electronic patient records, health smart homes, health applications and social media.
• What were the main findings?

  • ○
    The findings highlight the role of digital self‐efficacy, problem‐solving ability and organisational support in influencing the readiness to use a variety of digital health technologies, some of which are increasingly being used, particularly among older and less digitally exposed nurses.
• Where and on whom will the research have an impact?

  • ○
    Findings support the integration of digital health capability frameworks in combination with pedagogical approaches into quality improvement initiatives both in clinical and academic settings, requiring coordinated academic practice partnerships to build a digitally capable nursing workforce that is aligned with healthcare transformation goals.
• What does this paper contribute to the wider global clinical community?

  • ○
    Identifies digital readiness disparities among nurses, particularly in technologies beyond electronic health records—such as smart home systems, remote monitoring and telehealth—which are increasingly critical in outpatient and community‐based care.
  • ○
    Demonstrates that digital engagement is influenced by confidence, problem‐solving ability and help‐seeking behaviour, offering insights for targeted capacity‐building strategies in both education and service settings.
  • ○
    Reinforces the shared responsibility of academic institutions and healthcare organisations to embed digital health capability frameworks into both pre‐registration curricula and in‐practice training—ensuring nurses are equipped for evolving models of digitally enabled care.

1. Introduction

The use of digital health technologies is now foundational to address intractable healthcare issues and improve client quality health outcomes within the constraints of nurse shortages and socio‐political and financial uncertainty (Booth et al. 2021; Parker et al. 2018; World Health Organization and WHO 2021). This necessitates a deeper understanding and integration of technologies by nursing professionals (Australian Digital Health Agency 2020; ACIITC 2014). Nurses are the largest professional group in the health workforce and play a critical role in adopting, using, and implementing digital technologies as part of clinical care delivery. To meet the rising expectations for efficient and effective healthcare, nurses need to navigate mobile health applications, electronic health records (EHR), telehealth, and telemedicine to facilitate the collection of digital health data and sharing of health information (Australian Digital Health Agency 2020; ACIITC 2014).

1.1. Background

The integration of digital health solutions aligns with the strategic visions of healthcare leaders who view digital technologies with potential predictive modelling as a key component of holistic care coordination (Australian Digital Health Agency 2020; ACIITC 2014; Stoumpos et al. 2023). Utilising digital health technologies has shown potential to improve patient care coordination, reduce risk and improve quality health outcomes while also enhancing the cost‐effectiveness of care delivery, potentially boosting profitability for the healthcare sector. Despite this growing emphasis on digital health technologies in care delivery, significant gaps exist in the digital readiness of nurses and nursing students. Studies examining nurses' readiness to use digital health technologies emphasise that nurses lack specific technical skills, and that targeted education is needed for nurses and nursing students to ensure effective, efficient, and safe care as digital health technologies become ubiquitous in varied care settings (Booth et al. 2021; Hübner et al. 2018; Kleib et al. 2023; Livesay et al. 2023).

Digital readiness, as defined by Hammerton et al. (2021), refers to ‘the motivation and competence to effectively adopt, use, and spread digital health technologies’. Digital health competencies are essential for nurses to successfully integrate and utilise these technologies in clinical practice (Longhini et al. 2022). Although nurses are at the forefront of direct patient care and primary end users of newly introduced technologies—they are often excluded from the planning, procurement, and trialling of such technology (Risling and Risling 2020). Limiting or omitting nurses from participating in digital health development and implementation can potentially negatively impact patient care safety and data accuracy (Flott et al. 2021; Ibrahim et al. 2024). Existing technology readiness frameworks often lack consistency, with overlapping competency areas creating challenges in developing clear strategies for digital workforce preparation (Nazeha et al. 2020). However, patient safety is now inextricability linked to digital transformation (Flott et al. 2021), and the rapid evolution of digital health technologies, particularly those driven by artificial intelligence, demand that nurses develop digital literacy, self‐efficacy, and a strong grasp of relevant terminology.

In addition, with a shift towards incorporating remote health monitoring to support ageing in place, wearables, and telehealth across the healthcare delivery spectrum, there is a concerted effort to use digital health technologies to address intractable healthcare issues and improve client outcomes within the constraints of nurse shortages and socio‐political and financial uncertainty (Parker et al. 2018).

2. The Study

2.1. Aims

However, significant gaps remain in understanding nurses' digital health technology readiness (Australian Digital Health Agency 2020; ACIITC 2014). This knowledge is needed because digital health technology readiness may impact adoption and nurses' ability to effectively utilise these technologies in practice. Low readiness may inhibit quality care and result in additional clinical risk and inefficiencies (Syed et al. 2023). Accordingly, the purpose of this study was to explore the digital health technology readiness of nurses, nursing students, nurse academics and nurse leaders in Australia, aiming to identify strategies to bridge readiness gaps and ensure alignment with rapid technological advancements.

2.2. Research Questions

1. What is the digital readiness of nurse‐academics, and what are the factors that impact readiness to integration into the curriculum and courses, and what are the factors that impact readiness of using digital health technologies in curriculum and courses?

2. What is the digital readiness of Registered Nurses and nursing students, and what are the factors that impact the readiness of using digital health technologies in clinical practice?

2.3. Methodology

2.3.1. Design

A two‐phase, cross‐sectional, multiple methods study design was used. The quantitative arm included an online survey with one open‐ended question. Participants who completed the survey were invited to participate in a web‐based one‐on‐one interview. All data sources were triangulated to address the study questions to provide a more complete picture of the results.

2.3.2. Study Setting and Sample

Sample size of 209 for a population of 10,000 people (alpha = 0.01; t = 2.58) was calculated a priori (Barlett et al. 2001). Convenience and purposive sampling were used to target Registered Nurses, nursing students, nursing academics, and nurse leaders across Australia. Industry stakeholders consisted of nurses in leadership such as Chief Executive Officers (CEO), educators, managers, and directors. After ethics approval (HREC: A221737), a rolling recruitment was conducted with participants accessing the survey either by scanning a QR code or hyperlink. The sample size for the qualitative data strand was guided by Hennink and Kaiser (1982) with the aim of conducting 12–13 interviews. To minimise selection bias, social media platforms and nursing organisations such as the Australian College of Nursing were employed, and the Australian Council of Deans was used to disseminate recruitment materials.

2.4. Ethical Considerations

This study received ethical approval from University of the Sunshine Coast Human Research Ethics Committee (HREC: A221737). Informed consent was obtained from all participants through an online survey platform prior to participation.

2.5. Instrument

A variety of tools and scales that measure various aspects of digital readiness are discussed in the literature (Parasuraman 2000; Venkatesh et al. 2003; Scherrenberg et al. 2023). However, we needed an evaluation tool that measures digital innovation and readiness for the categories of digital health technologies of interest to us, and in a user‐friendly manner. Based on this, we used an alternative tool which utilises a selection of person‐reported experience measures created by R‐Outcomes Ltd. This concise and straightforward reliable and valid tool allowed the customisation for specific technologies (Hammerton et al. 2021, 2022; Michie 2014; Prensky 2001; Benson 2019).

2.6. Data Collection

2.6.1. Quantitative

In this study, participants responded to Likert scale questions (Strongly Agree, Agree, Neutral or Disagree) across each digital health technology area. Each section of the survey has four items and four responses each. Each item was scored on a scale from 0 (disagree) to 3 (strongly agree) (see Supporting Information). Cronbach alpha calculations were conducted to assess the internal consistency of responses within each technology category. The primary outcome, digital readiness, is a composite score derived from the four constructs of digital confidence, openness to innovation, help‐seeking behaviours and problem‐solving ability. These scores were calculated across digital health technology domains, including telehealth, wearables, electronic patient records, health smart homes, health applications and social media. Socio‐demographic information including age, ethnicity, sex, professional role and educational background information was obtained. The Modified Monash Model (MMM) (Department of Health and Aged Care 2019) was used to classify the metropolitan, regional, rural and remote areas and town size according to geographical remoteness.

2.6.2. Qualitative

After completing the quantitative survey, participants were able to opt in to a 1:1 interview. A total of eight interviews were conducted using semi‐structured questions, lasting between 45 and 60 min, with only the participant and interviewer being present during the session. All participants that expressed interest in the interview were interviewed. Individual interviews were recorded and transcribed. Field notes were not made, and transcripts were not returned to participants, and participants did not provide feedback on the findings. There was no prior relationship established prior to the study commencement. Pseudonyms were used in transcripts. The semi‐structured interview questions (Supporting Information) were tailored to the specific participant groups, centring around experience using digital health technologies, preparation, and differences in digital readiness.

2.7. Data Analysis

2.7.1. Quantitative Data Analysis

The analyses were conducted using STATA BE 18.5 software. Primary analyses focused on evaluating differences in digital readiness scores across demographic groups and exploring relationships between readiness constructs. We hypothesised that digital readiness scores would vary across demographic characteristics and that constructs such as help‐seeking and problem‐solving would be positively associated. Demographic characteristics were summarised using counts and percentages; Likert responses were summarised using medians and interquartile ranges. Given the ordinal nature and non‐normal distribution of the data (assessed via Shapiro–Wilk tests), non‐parametric methods were applied. Kruskal–Wallis tests compared independent group medians across demographic variables; Dunn's post hoc tests (Bonferroni‐adjusted) identified any significant pairwise differences. Subgroup analyses (e.g., by age and qualification) were exploratory and conducted post hoc in response to emerging patterns in the data; they were not pre‐specified in the study design. Spearman's rank correlation was used to assess associations between ordinal readiness scores across nine digital domains. All tests were two‐tailed, and statistical significance was defined a priori at p < 0.05. Confidence intervals for Spearman's correlation coefficients were estimated using non‐parametric bootstrapping with 1000 replications. This approach was chosen to account for the ordinal nature of the data and avoid assumptions of normality. Of 165 responses, three were excluded due to near‐complete missingness. One case with 56% missing data but a valid free‐text justification was retained; two others (56% and 67%) without justification were removed, leaving a final sample of 160. Across the dataset, only 123 of 7155 values were missing (1.72%). No outliers were identified, no sensitivity analyses were performed, and given the low rate of missingness, imputation was not applied.

2.7.2. Qualitative Data Analysis

All interviews were recorded and transcribed using online transcription software and checked for accuracy. A six‐phase reflexive thematic analysis process was used for analysis: (1) familiarisation with the data, (2) generation of initial codes, (3) grouping codes into themes and assigning relevant data to each, (4) reviewing themes for coherence and saturation, (5) refining themes and defining their meanings and (6) preparing the report with selected extracts and relating the analysis to the research question (Clarke and Braun 2013). Two researchers (MEH, AC) generated the themes and then met with other team members, who independently reviewed the data before convening to reach a consensus on the identified themes. Summative content analysis (Hsieh and Shannon 2005; American Association of Colleges of Nursing 2021) was used to analyse one open‐ended survey response. Data were reviewed and summarised by two researchers (GD, AC) and main keywords, content, and interpretation of context were compared and discussed to determine final themes. Participant response to an open‐ended response option ‘comments’ was narratively summarised by AC.

3. Results

3.1. Quantitative Findings

3.1.1. Demographics

A total of 160 survey responses were analysed. Demographic information is presented in Table 1. Cronbach Alpha Values for the nine technology domains and four constructs (confidence, openness to learning, help seeking, problem solving) indicated that the scales used were reliable, with most values in the ‘good’ to ‘excellent’ range, suggesting the items within each domain were well‐correlated and consistently measured the intended constructs.

TABLE 1.

Demographics.

Variable	Category	N	Percent
Age range	20–29	10	6.25
	30–39	35	21.88
	40–49	35	21.88
	50–59	57	35.62
	60 Plus	23	14.38
Sex	Male	18	11.25
	Female	141	88.13
	Prefer not to say	1	0.63
Highest qualification	Certificate of nursing	9	6.62
	Grad Certificate or Diploma	46	28.75
	Bachelor's degree	47	29.38
	Master's or PhD	58	36.25
Professional group	Nurse academic	16	10
	Nurse executive	33	20.62
	Registered Nurse	99	61.88
	Student Nurse	12	7.5
Practice setting	Acute care	57	35.62
	Community	32	20
	Other	6	3.75
	Outpatient/Sub acute/Rehab	10	6.25
	Residential care	6	3.75
	Telehealth	25	15.62
	Tertiary education	24	15
Area of practice	Metropolitan	78	50.32
	Regional rural or remote	77	49.68
Graduation year	1975–1990	19	11.95
	1991–2000	25	5.72
	2000–2010	29	18.24
	2011–2020	60	37.74
	2020—	26	16.35

3.1.1.1. Digital Readiness

The overall digital readiness score was calculated as the average of Likert scores across four constructs—confidence, openness to learning, help‐seeking and problem‐solving—within each technological domain (Figure 1). Although moderate to high levels of digital readiness were generally observed across the nine domains, substantial variability was noted between respondents. Among the domains, smart home technology had the lowest average readiness score, at 49.29.

FIGURE 1.

Overall digital readiness score across domains.

3.1.1.2. Confidence

The construct of ‘Confidence’ was derived from Likert responses to the statement ‘I am confident…’ across eight technology domains. As shown in Figure 1, overall digital confidence was moderate to high, with the lowest scores observed for smart home technologies and the highest for information technology and telehealth. No significant differences in overall confidence were found by geographic setting, profession, or graduation year. Although differences by age and qualification level were initially observed, these did not remain significant after Bonferroni correction for multiple comparisons (Table 2).

TABLE 2.

Constructs (confidence, openness, help seeking and problem solving) across all domains by demographic and professional groups.

Variable	N groups	N observations	Chi‐square (Kruskal–Wallis test statistic)	df	p
Reported confidence levels across all tech domains
Geographic location	2	155	3.189	1	0.074
Grad year	5	159	7.001	4	0.136
Age range	5	160	12.4	4	0.015*
Practice setting	7	160	8.312	6	0.216
Qualification	4	160	8.373	3	0.039*
Profession	4	160	0.685	3	0.877
Reported openness to learning across all tech domains
Geographic location	2	155	1.691	1	0.091
Graduation year	5	159	9.155	4	0.057
Age range	5	160	14.569	4	0.006#
Practice setting	7	160	6.126	6	0.408
Qualification	4	160	15.905	3	0.001#
Profession	4	160	0.795	3	0.851
Reported help seeking ability across all tech domains
Geographic location	2	155	0.2478	1	0.248
Graduation year	5	159	13.688	4	0.008#
Age range	5	160	17.829	4	0.001#
Practice setting	7	160	4.915	6	0.558
Qualification	4	160	4.915	3	0.555
Profession	4	160	3.282	3	0.350
Reported problem solving ability across all tech domains
Geographic location	2	155	0.6391	1	0.641
Graduation year	5	159	13.682	4	0.008#
Age range	5	160	23.794	4	0.0001#
Practice setting	7	160	4.053	6	0.669
Qualification	4	160	8.185	3	0.042#
Profession	4	160	1.539	3	0.673

Note: Kruskal–Wallis test results for differences in reported confidence, openness to learning, help‐seeking and problem‐solving ability across demographic and professional variables. Post hoc pairwise comparisons (where applicable) used Dunn's test with Bonferroni correction. Asterisk (*) indicates statistical significance before correction; hash (#) indicates significance after correction.

Abbreviations: df, degrees of freedom; N, number.

3.1.1.3. Openness to Learning

Openness to learning did not vary significantly by practice or geographic setting. However, as shown in Table 3, Figure 2, lower scores were observed among respondents aged 60+ compared to those in their 20s and 30s, and respondents with bachelor's degrees scored higher than those with other qualifications.

TABLE 3.

Descriptive statistics for significant variation in reported construct scores.

	N	Mean	Median	95% CI upper/lower	Significant pairwise differences with …
Openness to learning (Age)
20–29	10	84.88	85.28	76.5/93.25	60 plus
30–39	35	79.05	85.33	71.7/86.41	60 plus
40–49	35	76.48	81.67	69.25/83.72	None
50–59	57	70.04	70.44	64.59/75.49	None
60‐plus	23	60.9	63.22	50.35/71.45	20–29, 30–39
Openness to learning (Qualification)
Certificate or year 12	9	60.11	70.44	42.96/77.26	Bachelor's degree
Graduate diploma	46	70.83	68.72	64.86/76.81	Bachelor's degree
Bachelor's degree	47	82.21	87.5	76.33/88.09	Grad dip, Certificate, MA or PhD
Master's or PhD	58	69.35	72.39	63.47/75.23	Bachelor's degree
Help seeking (Age)
20–29	10	80.47	81.61	48.05/61.77	60 plus
30–39	35	75.52	77.78	48.05/61.77	50–59
40–49	35	68.19	63.22	48.05/61.77	none
50–59	57	58.77	63.22	48.05/61.77	30–39
60‐plus	23	57.29	59.56	48.05/61.77	20–29, 30–39
Help seeking (Graduation year)
1975–1990	19	49.59	48.11	37.97/61.21	2011–2020
1991–2000	25	59.99	59.44	51.18/68.81	None
2001–2010	29	67.79	63.22	58.95/76.63	None
2011–2020	60	71.59	74	65.65/77.54	1975–1990
2020 plus	26	68.62	68.67	59.73/77.5	None
Problem solving (Graduation year)
1975–1990	19	45.3	45.75	33.71/56.88	None
1991–2000	25	54.05	54.38	43.11/64.99	None
2001–2010	29	64.53	63.22	54.76/74.3	None
2011–2020	60	67.48	70.5	61.33/73.63	1975–1990
2020 plus	26	62.9	65	54.69/71.11	None
Problem solving (Age range)
20–29	10	75.66	74.22	64.79/86.52	60 plus
30–39	35	73.39	70.67	65.94/80.83	60 plus, 50–59
40–49	35	64.4	63.22	56.99/71.82	60 plus
50–59	57	55.46	55.56	48.48/62.43	30–39
60 plus	23	45.3	44.56	34.95/55.65	20–29,30‐39,40–49
Problem solving (Qualification)
Certificate or Year 12	9	57.02	59.44	37.97/76.06	none
Graduate diploma	46	61.46	61.33	53.74/69.17	none
Bachelor's degree	47	69.3	70.33	63.05/75.55	Master's, PhD
Master's or PhD	58	54.91	55.72	48.05/61.77	Bachelor's degree

Note: NB no significant variation across groups seen in the ‘confidence’ construct.

Abbreviations: CI, confidence Interval; N, number of observations.

FIGURE 2.

Levels of openness to learning in relation to (a) age and (b) qualification level.

3.1.1.4. Help Seeking

Help‐seeking scores varied by age and graduation year (Table 2). Respondents aged 60+ scored lower than those in their 20s and 30s, and those who graduated after 2010 scored higher than respondents who graduated prior to 1990 (Figures 3b and 4a). No differences were observed by profession, qualification, practice setting, or geographic location (Table 3).

FIGURE 3.

Levels of help seeking in relation to (a) age and (b) year of graduation.

FIGURE 4.

Problem‐solving ability in relation to (a) age, (b) year of graduation and (c) qualification level.

3.1.1.5. Problem Solving

Reported problem‐solving ability varied across age, graduation year and qualification. Respondents aged 50 and older reported lower scores than those aged 20–39 (Figure 4a), and those who graduated after 2010 scored higher than earlier graduates (Figure 4b). Interestingly, respondents with Master's or Doctoral degrees reported lower problem‐solving scores than those with Bachelor's qualifications (Figure 4c). Full results are presented in Tables 2 and 3.

3.1.1.6. Correlations Among Constructs Across Contexts and Technologies

The relationships between the four constructs (reported confidence, openness to learning, help seeking and problem‐solving ability) were also explored. The strongest positive correlation was observed between help seeking and problem solving (ρ = 0.91).

Confidence also showed a strong positive correlation with openness to learning (ρ = 0.84), help seeking (ρ = 0.82) and problem solving (ρ = 0.81) (Table 4). The strength of the association between help seeking and problem solving was consistently high across individual technologies, ranging from ρ = 0.63 in information technology to ρ = 0.91 in social media and wearables (all p < 0.001).

TABLE 4.

Spearman's rank correlations between digital readiness constructs (N = 160).

Construct pair	ρ (Spearman)	95% confidence interval
Confidence—Openness	0.84	0.79 to 0.90
Confidence—Help‐seeking	0.82	0.76 to 0.88
Confidence—Problem‐solving	0.81	0.75 to 0.87
Openness—Help‐seeking	0.78	0.72 to 0.85
Openness – Problem‐solving	0.79	0.73 to 0.84
Help‐seeking – Problem‐solving	0.91	0.88 to 0.95

Note: 95% confidence intervals calculated using nonparametric bootstrapping (1000 replications). All correlations were statistically significant (p < 0.001).

3.2. Qualitative Findings

3.2.1. Open‐Ended Question Data

Of the 160 survey respondents, 43 provided a short statement to an open‐ended response option entitled ‘comments’. Respondents identified a need for more comprehensive education and training to ensure that nursing students and healthcare professionals are well‐prepared for the digital environment. The importance of ongoing support, training, and updating of hardware was highlighted across responses, as well as the need for usability improvements and integration of digital tools. Leadership and cultural change management were identified as essential factors for successful implementation and ongoing success. Accessing healthcare using digital tools was communicated as potentially inequitable due to the financial constraints of patients and limited internet access in certain areas. There was recognition of the connection between the need to expand digital solutions to manage overcrowded health services. However, respondents expressed a prevailing sense of optimism for the potential of digital health technologies to improve patient care, enhance access to care and drive innovation in the healthcare sector.

3.2.2. Interview Data

Eight participants provided their email for the participation of a 1:1 interview. The interviewees were all female, spanning a wide aged range from 30 to 69 years, with the majority aged between 60 and 69 years. They represented diverse roles within nursing, including two student nurses, clinical and acute care nurses, educators, academics and a senior leadership position as a Chief Nursing and Midwifery Information Officer. Their professional qualifications ranged from high school to PhD, with most holding bachelor's degrees or higher. Geographically, participants were predominantly from regional areas, with two based in metropolitan locations. Practice settings varied widely, including acute care, mental health, general practice, academia and outpatient clinics. Four main themes emerged from the data: ‘Opportunities for efficient ways of working’, ‘digital technology turning experts into novices’, ‘disillusionment between expectation and reality’ and ‘shared responsibility for development of digital expertise’. Results for each theme are outlined below using participant voice.

3.2.2.1. Opportunities for Efficient Ways of Working

All forms of automation through technology were perceived as efficient and effective. Many described how digital records offered faster access to information, improving resource use, with one source of truth accessible to all users at the same time. One participant explained: ‘[ieMR] enables everything to be gathered in one place… I can be in a chart same time that physio and doctor, whoever else needs to be there …it enables us to have one source of truth and it's legible’ (P5).

Having all information in one place helped nurses provide timely care and changed how they worked, making their roles a bit easier day‐to‐day. Some mentioned benefits like better chronic disease management and improved remote monitoring. As one participant put it, ‘Digital technologies help enable clinical decision making at the point of care’ (P6). Timesaving was a common observation. One participant reflected: ‘…the [digital] technology …saves a lot of time and so, … You have enough time to give the best care for your patients’ (P3). Others noted improvements in tasks that used to be time‐consuming: ‘…I can run a report in 5 min that used to take me days and days of going through charts’, (P5). Reducing wait times, especially for non‐urgent care, and improving outcomes through ongoing patient support were considered as major benefits of using digital tools in practice.

3.2.2.2. Digital Technology Turning Experts Into Novices

Participants highlighted how digital technologies turned experienced nurses into novices, leading to emotional exhaustion, frustration and resignation. One participant recalled, ‘I was working with nurses who had been …in that particular role for 35 and 40 years, and they were very challenged and went instantly from expert to novice’ (P8). The introduction of electronic health record systems requiring extensive computer literacy, contributed to senior nurses exiting the workforce. As the same participant added ‘…more senior staff …are fairly trapped, and if they could have left by now, they would have, …that kind of leaves us with more bitterness at that end, more emotional exhaustion…’ (P8). The shift from paper‐based to computer‐based documentation has drastically changed the nature of nurses' work, challenging the sense of professional identity.

The impact of this shift was shared by a participant: ‘…people are resigning. Really good senior staff because they say nursing is not what it was. This is not what I signed up to do, to sit in front of a computer screen all shift’ (P1).

This contributed to professional resistance as participants cautioned that technology reduces the human connection in nursing. Participants attributed this resistance to a mindset that values physical presence and tangible interactions over virtual care, as one noted: ‘…not everyone is embracing it, because some people are still stuck. …they believe that as a nurse, if I'm not working in the hospital, if I'm not doing wound dressing and immunisation, …I'm not administering care physically…’ (P1).

Nurses have traditional ways of working and when digital technology is introduced, new workflows need to be developed. Participants cautioned against simply digitising old processes, highlighting the need to involve clinicians in workflow redesign. One suggested: ‘…utilising the knowledge base and the needs of clinicians in developing workflows and avoid just digitising paper’ (P5). Digital roles were not viewed as ‘real’ nursing. Becoming a digital health nurse was compared to ‘… crossing over to the dark side. …moving away from, …front line care’ (P6). Participants suggested that generational differences influenced how nurses engaged with digital tools. Older staff were perceived to struggle more with operating new digital technology: ‘…especially clinicians aged over about 50, …They just could not get their head around it’ (P4). Younger staff were often seen as more comfortable with technology, as one participant observed: ‘…the younger ones, … seem to have less trouble with it. …the digital natives cope with it easier. The young ones pick up the shortcuts quicker’ (P2).

3.2.2.3. Disillusionment Between Expectation and Reality

This theme highlights the challenges and frustrations nurses face when using digital technology that is intended to streamline processes but often fails to work effectively, resulting in poor integration and a preference for the old or traditional methods. Participants suggested that nurses are unlikely to adopt technology that they do not understand, that does not function as expected, or that takes too much time. As one noted: ‘If you're using it properly, it [patients' Observations & ECG] can go straight to the ieMR [referring to the electronic health record system] …But they mostly don't work. They don't transmit, so, I never bother, too much hassle with that. But that's, ideally meant to save time’ (P2). When technology makes their work harder, it leads to frustration and workarounds, which may compromise safety.

Despite the benefits of digital technologies like electronic health and medical records, concerns were raised about potential errors and adverse events from incorrect data entry and reduced bedside interactions between nurses and patients.

One participant described the pressure to get it right and avoid triggering a medical emergency: ‘…If you've put a value in the wrong spot, you can initiate a MET [medical emergency team] call. …then you've got to ring the MET team and say, sorry, that was an error. … there're so many potentials for error with it’ (P2). Participants warned that documentation in electronic health and medical records reduces direct bedside interactions between nurses and patients.

As one noted, ‘Normally… [nurses will] be at the bedside talking to the patient, writing the chart. … now they're all out in the corridor with their big machines… they keep the machines at the doorway, and they talk to the patient from the doorway’ (P4). This shift away from bedside presence was viewed as risky, with one participant warning: ‘…I can see the huge potential for adverse events … [the nurse] wouldn't have looked at [the patient] for maybe another 5 min and seen that [they weren't] breathing’ (P4). Another concern was raised: ‘… the attention of particularly the medical staff who kind of congregate over each other's shoulders looking at the computer rather than looking at the patient in the bed’ (P8).

3.2.2.4. Shared Responsibility for Professional Development of Digital Expertise

Significant gaps were identified in nursing education regarding digital health technologies. Participants revealed that their education and training did not adequately prepare them for the use of digital tools in healthcare settings and called for formalised integration of digital health components from the outset of nursing education. One claimed: ‘To be honest, I don't think the training did much in preparing … There should be a unit specifically dedicated to [digital healthcare]’ (P1). Others highlighted the need for continuous professional development opportunities focused on digital readiness to bridge the existing gap between traditional nursing roles and emerging digital healthcare practices. As one participant reflected: ‘…I feel like my 3 years of nursing is just getting me ready, … I just gain basic knowledge, and the grad year will be the continuation of those, and that will expose me to more on those technologies’ (P7).

Contrasting views emerged regarding the need for students to experience up‐to‐date digital technologies in clinical lab settings versus developing a broader conceptual understanding that could be applied across any models of equipment. Students expressed anxiety about their potential to make mistakes with technology during clinical placements highlighting the gap between academic preparation and the demands of a fast‐developing clinical environment. As highlighted by a participant: ‘A university should be able to know what is the new technologies that are being used. Let students know … add it to the curriculum, … let us know some of the new technologies … we use in the hospital, … just to give students an idea of what they are going to face when they start their placement’ (P3). Trying to keep academic programs aligned with fast‐changing health technologies was seen as a considerable challenge.

As one participant pointed out, ‘There are so many different types of health technologies. I think it would be really challenging for a nursing curriculum to cover them all, because they also turn over and advance so quickly’ (P6). To keep teaching material relevant and current, one academic shared how they regularly check in with clinical staff during content development: ‘I constantly use my sessional [staff] to go, ‘Is there anything that's outdated?’ … as I was writing new content’ (P4).

Suggestions for optimising the integration of digital health technologies in nursing curricula included stakeholder consultation and collaboration with clinical staff as change champions. Shared responsibility for nursing workforce preparation between universities and industry was emphasised. One participant explained, ‘…it's not bad for them to be really learning it while they're in clinical practice, because they're dealing with real patients, real conditions, real questions, real problems… my experience has been that in a couple of weeks …they're very good at it. …I don't see a problem with pushing it [digital training] into the work setting’ (P4).

Although universities were considered responsible for laying the foundations, participants highlighted that gaining hands‐on experience and learning in clinical environments is as vital. One participant reflected on the important role of professional bodies and the need for clearer pathways: ‘Graduate certificate by APNA and ACN… They should be talking about some of these things. Even as an undergrad, it should be a thing… I should be able to say I want to be a digital tech nurse… If digital technology was part of the training, then you can even have a postgraduate …a nurse consultant in digital tech’ (P1). Reinforcing this, the same participant noted, ‘I think more awareness, more education, to help professionals begin to see the potential’ (P1).

4. Discussion

This study revealed a readiness gap among nurses which may have critical implications for patient safety and care quality. Low confidence among nurses around the use of electronic health records, in‐home monitoring technology, and other wearable technologies could increase clinical risks if real time patient monitoring, effective documentation, and care coordination are compromised. In our study, younger nurses, recent graduates and late entrants to the profession reported higher levels of confidence and openness to learning in contrast to more experienced nurses who reported lower confidence.

Being exposed to technology in previous careers may explain the increased readiness among nurses who changed from a previous career to nursing. These findings suggest that professional development for nurses educated before the digital health era may be insufficient (Kleib et al. 2023; Livesay et al. 2023). Interestingly, nurses with bachelor's degrees seemed more open to learning about digital health compared to nurses with post‐graduate qualifications, contrasting with previous findings (Kleib et al. 2023; Konttila et al. 2019; Kuek and Hakkennes 2020; Peltonen et al. 2016). However, older age is highlighted in the literature as a barrier to confidence and uptake of digital health in clinical practice (Scherrenberg et al. 2023; Konttila et al. 2019; Kuek and Hakkennes 2020; Rogers et al. 2014). An explanation for this may be that about 30% of participants in our study identified themselves as either nurse executives or nurse academics, with about 36% having either a Masters or Doctoral degree. These individuals may have limited exposure to emerging digital health technologies in practice and may pursue advanced degrees later in their careers (Buerhaus et al. 2017). However, in addition to age, other organisational factors are reported as contributing, such as limited access to or inadequate training, concerns that digital tools increase administrative burden in already difficult workload conditions, and lack of engagement in a culture of openness to change (Mather and Cummings 2019; Woods et al. 2023).

Nurse executives, whereas aware of the need for workforce readiness, may lack the digital fluency to lead transformation (Booth et al. 2021; Kleib et al. 2023). Confidence varied by the type of digital health technology. In‐home monitoring ranked lowest, likely due to limited exposure to remote health monitoring using smart homes. Confidence was higher for IT systems, telehealth and wearables, consistent with existing research (Konttila et al. 2019; Peltonen et al. 2016; Buerhaus et al. 2017). The COVID‐19 pandemic accelerated the adoption of telehealth across the profession. Subsequently, nurses and other clinicians required greater levels of digital readiness as telehealth was increasingly embedded into routine workflows. This shift likely impacted the recognition of the value of digital health technologies to care coordination and continuity (Monaghesh and Hajizadeh 2020; Gajarawala and Pelkowski 2021; Rauschenberg et al. 2021). In alignment with the extant literature, nurses in this study also reported being familiar and confident in using wearables and supporting patients with technologies like smartwatches and fall pendants (Rauschenberg et al. 2021; Seibert et al. 2020). However, as health systems move to support ageing in place, remote monitoring powered by AI will become more common, necessitating greater levels of nurse literacy in digital health (Fritz and Dermody 2020). Accordingly, a shared responsibility for preparing the nursing workforce is needed.

4.1. Implications for Policy and Practice

Despite the establishment of the National Nursing and Midwifery Digital Health Capability Framework to guide education and workforce development, the integration into nursing curricula remains inconsistent and slow (Australian Digital Health Agency 2020; ACIITC 2014; Cummings et al. 2021; Baron 2017). This is concerning because many digital health technologies are increasingly being used across care settings including community‐based settings. Whilst new graduate nurses are novices upon graduation, they should have some exposure and awareness of these technologies to enhance their readiness to adopt new technologies as they emerge (Australian Digital Health Agency 2020; ACIITC 2014). Nurses also need to know how to educate their patients and family caregivers. If low confidence results in lower adoption readiness among nurses, they will unlikely recommend them to their patients or clients (Clarke‐Darrington et al. 2023; Brown et al. 2020; Tischendorf et al. 2024). This curriculum lag is driven by multiple, intersecting challenges. For example, nursing programmes are already burdened by dense, compliance‐driven content requirements, making it challenging to integrate new knowledge, a phenomenon where an already full curriculum becomes even more overloaded, which may lead to the omission of important concepts potentially failing students to be prepared for real‐world technology‐enabled care environments (Giddens and Brady 2007). Similar to other findings that nursing students are not exposed to digital health technologies sufficiently (Booth et al. 2021; Livesay et al. 2024), in this study nursing students reported that they have limited, if any, access to contemporary digital health technologies in academic settings beyond basic technologies such as electronic blood pressure cuffs, battery‐based thermometers, or pulse oximeters.

Another explanation for the lag in integrating digital health into the nursing curriculum is likely due to many nursing academics having been educated before competencies around digital health technologies were developed and became relevant to their practice (De Leeuw et al. 2020). Digital health education in nursing often lacks a clear pedagogical framework, relying on traditional teaching models that may not align with the complex, evolving nature of digital health. Although Livesay et al. (2024) highlight the importance of aligning nursing curricula with National Digital Health Capability Frameworks (Australian Digital Health Agency 2020; ACIITC 2014), the findings of this study suggest that there is a pressing need to pair capability frameworks with structured pedagogical models to guide the integration of digital health education across nursing programmes. And while nursing academics and clinicians are encouraged to self‐assess their digital readiness and pursue professional development, the lack of systemic support, infrastructure and protected time for capacity‐building continues to limit progress (Clarke‐Darrington et al. 2023; Brown et al. 2020; Tischendorf et al. 2024).

However, without policy advocacy for coordinated investment and cross‐sector support, the nursing profession risks being left behind in preparing graduates for digitally enabled practice environments that are rapidly evolving across all sectors of healthcare. Universities need to partner with industry, including acute and community care, to find ways to address the mismatch between rapidly evolving digital health technologies and the clinical and academic nursing workforce's digital health capability using concept‐based curricula, rather than layering it on top of existing content (Benner et al. 2010; Morris et al. 2023). Health tech innovators and companies that produce digital health technologies used in various clinical practice settings should partner with academic nursing programmes to support nursing students as a critical ‘next user’ in the digital health and care ecosystem. With patient safety being inextricably linked to digital transformation and subsequently clinical workflows, nurses must not only be digital health literate but also included in the design and implementation process of these technologies (Booth et al. 2021; Risling and Risling 2020; Topaz and Pruinelli 2017; Nowrouzi‐Kia et al. 2024).

Future research should explore longitudinal trends in digital readiness and evaluate targeted interventions to boost confidence and competence in digital health technologies. There is a paucity of knowledge around what types of digital health technologies nursing students are exposed to in their nursing studies, and what pedagogical approaches are well suited to integrating this knowledge into the curriculum. Future studies should also examine the relationship between nursing students' exposure to digital health technologies during their nursing studies and their confidence level, and openness to adopting new technologies in practice.

4.2. Study Strengths and Limitations

The multiple methods approach was a strength of this study. Sample sizes did not meet predefined targets in both the quantitative and qualitative data strands, and the results of this study need to be viewed with caution. However, it remains within the range of comparable studies examining digital readiness in health settings. For example, Hammerton et al. (2022) surveyed 203 general practice staff using the same R‐Outcomes instrument, whereas Kuek and Hakkennes (2020) surveyed 407 healthcare staff across two Australian public hospitals to assess digital literacy and attitudes towards information systems. Convenience sampling may have introduced selection bias, as participants with a stronger interest in digital health technologies may have been more likely to respond. This could lead to an overestimation of digital readiness levels, particularly for constructs like confidence and openness to innovation. As all the data were collected via self‐report at one time point, there is a risk of common methods bias. The strong correlation between help seeking and problem solving may partially reflect this. However, the variation in this correlation across the domains explored suggests the relationship likely reflects a real link between the two behaviours. The cross‐sectional design captures data at a single time point and cannot establish causality between demographic factors and digital readiness constructs, such as help‐seeking and problem‐solving ability. Despite these limitations, the study provides valuable insights into the digital readiness of the nursing workforce and highlights areas for targeted interventions.

4.3. Conclusion

It is clear that while nurses are open to innovation, there are confidence gaps potentially limiting the successful integration of technology into clinical workflows. To address these gaps, nursing education and health care organisations have a shared responsibility in fostering a culture that supports digital skill development through collective efforts.

Author Contributions

Gordana Dermody: conceptualisation, methodology, investigation, writing of original draft, review and editing, project administration; Daniel Wadsworth: conceptualisation, methodology, formal analysis, visualisation, writing of original draft, review and editing; May El Haddad: conceptualisation, methodology, formal analysis, visualisation, writing of original draft, review and editing; Roslyn Prichard: methodology, formal analysis, data curation, visualisation, writing of original draft, review and editing; Alex Benson: project administration, resources, investigation, data curation; Tim Benson: conceptualisation, methodology, resources, investigation, data curation; Alison Craswell: methodology, validation, formal analysis, writing of original draft, review and editing.

Disclosure

Patient and Public Involvement: Participants provided full informed consent electronically. The survey process and consent documentation were compliant with institutional ethics requirements and the National Statement on Ethical Conduct in Human Research (Australia).

Statistical Analysis: The authors confirm the following: (b) The statistical analyses were checked prior to submission by two members of the author team with expertise in quantitative methods: Roslyn Prichard (Email: rprichar@usc.edu.au) and Daniel Wadsworth (Email: dwadswor@usc.edu.au). (d) The authors affirm that the statistical methods used are appropriate to the study design and data structure, and the findings have been correctly interpreted within the context of the research. (e) The authors accept full responsibility for ensuring the appropriateness and accuracy of the statistical analyses and interpretations included in this submission.

Ethics Statement

This study received ethical approval from the University of the Sunshine Coast Human Research Ethics Committee (HREC: A221737). Informed consent was obtained from all participants through an online survey platform prior to participation.

Conflicts of Interest

None of the authors received funding, compensation or financial benefit from R‐Outcomes. Alex Benson serves as a project manager at R‐Outcomes, and Tim Benson is the founder of R‐Outcomes and developer of the survey tool used in this study. All data analysis and interpretation were independently conducted by Gordana Dermody, Daniel Wadsworth, Roslyn Pritchard and Alison Craswell, without influence from R‐Outcomes. The authors take full responsibility for all analyses, findings and conclusions presented.

Supporting information

Data S1: jan70105‐sup‐0001‐Supinfo.docx.

Data Availability Statement

Data are not publicly available due to privacy considerations related to participant consent and data handling procedures.