Health information technology foresight for Iran: A Delphi study of experts’ views to inform future policymaking

Abstract

Background:

Policymakers require a systematic approach when planning for information technology needs in healthcare.

Objective:

The aim of this study was to obtain experts’ predictions of future health information technology (HIT) needs until 2025 for Iran in relation to the relative importance of key technologies, expected timeframe of realisation, areas that may be impacted upon and obstacles to achieving these goals.

Method:

This article presents results from the third phase (a Delphi study) of a larger mixed-method study. Policymakers from the Iranian Ministry of Health and faculty members from different medical universities across the country who were expert in the field of HIT were invited to participate (n = 61).

Results:

Participants (39) completed the first-round questionnaire and 24 completed the second. The development of personal health records (n = 32, 82.0%), the development of clinical decision-making systems (n = 30, 76.9%) and the use of business intelligence for collecting and analysing clinical and financial data (n = 32, 82.0%) were predicted to occur after 2025. The healthcare areas predicted to experience the greatest impact from most HITs were facilitating patient–provider communication and improving healthcare quality. Key barriers to achieving HITs were related to weaknesses in planning and limited financial resources for most technologies.

Conclusion:

By identifying the areas of impact and the barriers to achieving the HIT goals, more accurate planning is possible and resources can be allocated according to priorities.

Keywords

health information technology foresight forecast policymaking decision-making health information management technology assessment health health policy policy analysis strategic planning health planning

Introduction

Health information technologies (HITs) include a wide range of products and services such as electronic health records (EHRs), telehealth, mobile health technologies, cloud-based services, medical devices and tele-monitoring tools, assistive technology and sensors. These technologies facilitate collecting, sharing and using health information for individuals, healthcare institutions and community-based healthcare organisations (Amatayakul, 2013; DeSalvo, 2016; Ehrenfeld and Cannesson, 2013). HITs also have the potential to prevent medical errors, reduce costs, increase efficiency, improve the quality of healthcare and empower patients and clinicians (Ehrenfeld and Cannesson, 2013). However, despite their value, the balance between the benefits and risks of using IT in the healthcare sector is still not entirely clear. Policymakers, who make key decisions about the potential purchase and implementation of HITs, need to be informed about the positive and negative impacts of HITs at different levels, such as individual (e.g. patient care) and organisational levels (e.g. hospital productivity) (Ayatollahi et al., 2009; Lee et al., 2013). They also need to be informed about potential barriers to implementation and realistic time frames.

Given the importance and financial implications of HIT, it is essential that policymakers and healthcare organisations are rigorous in their examination of possible issues around future use (Wyatt and Sullivan, 2005). To investigate these issues, one approach, known as the forecast approach, often does not clearly identify the future in a systematic manner, as forecasting is only an estimation of the future in a specific area and ends with determining possibilities (Hemmat et al., 2017; Staggers et al., 2013; Turley, 2002). Instead of the forecast approach, future research is a more systematic and rational study design of the future, which aims to identify possible prospects. Future research is also known as foresight, strategic foresight, prospective and prognostic studies and futurology (Staggers et al., 2013). Future studies provide different options for policymakers that assist them in choosing and designing the most desirable future (Slaughter, 2002). Foresight is one of the most widely used approaches in future studies and can be defined as:

the process involved in systematically attempting to look into the longer-term future of science, technology, economy, environment and society with the aim of identifying the emerging generic technologies and the underpinning areas of strategic research likely to yield the greatest economic and social benefits. (Martin, 2001: 34)

Technology foresight supports prioritising science and technology for promising investments in the future; the coherence and efficiency of the innovation system; and the creation of a common vision for the future of technologies, opportunities and strategies (Nabipour, 2011). Technology foresight is considered one of the most important elements of the technology development process. It provides inputs for setting technology strategies and policies and develops technology infrastructure. In addition, technology foresight supports innovation and public–private partnerships in the process of technology management and transfer (Hemmat et al., 2017; UNIDO, 2005).

A number of studies have been conducted about the future of HIT (Ahlqvist et al., 2007; Atkins and Cullen, 2013; Bates and Bitton, 2010; Cuhls et al., 2008; Mandl and Kohane, 2012; McGowan et al., 2012; Weiner et al., 2013; Wyatt and Sullivan, 2005); however, few foresight studies have been performed. Most studies have used the less systematic forecasting approach or reviewed existing trends (Hemmat et al., 2017). It is notable that the use of foresight studies in the field of HIT was proposed for the first time in 2002 (Englebardt and Nelson, 2002). A relevant study for our research in this area was a foresight study conducted in Germany exploring the future of information technology in the health sector using the Delphi method to collect data (Cuhls et al., 2008). Our study has been theoretically informed by foresight studies undertaken in the area of HIT.

In Iran, a notable study in predicting the future of HIT was formed as part of the development of a comprehensive scientific plan in 2007 (Riazi and Abedian, 2011). Methods used in this project were SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, STEEPV (Social, Technological, Economic, Environmental/Ecological, Political, Value-based issues) and brainstorming. However, the resultant map did not cover the specific aspects of the future of HIT required by policymakers. For example, according to this map, it was not clear which information technologies would change the health sector, what were the capabilities of these technologies and what the barriers to achieve these technologies were (Riazi et al., 2011; Riazi and Abedian, 2011).

The aim of the current study was to obtain experts’ predictions of future HIT needs until 2025 for Iran in terms of the relative importance of key technologies, expected time of realisation, areas that may be impacted and the obstacles to achieve them. This article presents results from the third phase (Delphi study) of a larger mixed-method study exploring future HIT needs. Phase 1 of the larger study aimed to review and develop a mind map of different types of HITs (Hemmat et al., 2017). In phase 2, semi-structured interviews were conducted (n = 13) to identify key HITs and related issues for Iran until 2025 (Hemmat et al., 2018).

Method

Research design

This study used the Delphi technique to predict the future HIT needs for Iran by obtaining opinions of experts through a series of questionnaires and feedback to reach a consensus.

Participants

Policymakers from the Iranian Ministry of Health and faculty members from different medical universities across the country who were expert in the field of HIT were invited to participate in the study (n = 61). To be eligible, all participants needed to have a doctoral degree in health information management or medical informatics, at least 5 years’ work experience in the field of HIT and familiarity with different types of HITs.

Questionnaire

Results from phase 1 (literature review) and phase 2 (semi-structured interviews) of the larger study (Hemmat et al., 2017; Hemmat et al., 2018) informed the development of the questionnaire for the current study, phase 3, the Delphi study. In the first round, the questionnaire consisted of five sections and 69 questions. Section 1 related to participants’ personal information (5 questions); section 2 was devoted to the importance of key HITs (16 questions), which were derived from phase 2 of the larger study (Hemmat et al., 2018) and used a five-point Likert-type scale questionnaire with scores ranging from not important (1) to very important (5); section 3 explored the expected time of realisation for each technology (16 questions), with options of from 2016 to 2020, from 2021 to 2025 and after 2025; section 4 related to the areas of impact for each technology (16 questions); and section 5 related to the barriers to achieve each technology (16 questions). In sections 4 and 5, the prioritisation method was used, with the lowest priority being (1) and the highest being (6).

In the second round of the Delphi method, those items that did not reach the experts’ consensus in the first round were identified and formed a new questionnaire. This questionnaire, along with the results of the first round of the Delphi study, was sent to the same participants. The second questionnaire consisted of three parts: the expected time of realisation for five technologies (5 questions), the areas of impact for each technology (16 questions) and the barriers to achieve each technology (16 questions). The Delphi questionnaires were designed electronically and sent to the experts by email.

Six experts in the field of health information management and medical informatics reviewed all of the questionnaire items for readability, clarity and comprehensiveness and confirmed the face and content validity of the questionnaire (Bolarinwa, 2015). Regarding the Delphi results, there is very little empirical evidence on the reliability or validity of the consensus process in the Delphi technique. The Delphi does not use expert panels to be a representative sample for statistical purposes (Powell, 2003). In fact, when selecting the experts for a Delphi study, it is important to consider their experience or knowledge to determine the reliability and validity of the results (Giannarou and Zervas, 2014). Other strategies to check the validity of Delphi results are assessing the internal logic (e.g. by checking the consistency of the group’s output) and assessing the face validity in terms of the usefulness and correctness of the results (Powell, 2003). In the current study, the experts were experienced in the field of HIT and the results were consistent across the group.

Data analysis

Descriptive statistics were used to analyse the data. For each item, a response rate of 75% or more on the categories of important or very important was considered necessary for reaching a consensus by participants and for inclusion of items in the final list. If the consensus was between 50% and 75%, the item was entered in the second round of the Delphi study. If the consensus was less than 50%, the item was removed from the final list of key HITs. According to Powell (2003), setting a percentage level for inclusion of items is a common interpretation technique.

Regarding the impact of key HITs and the barriers to achieve them, the mean value for each impact and barrier was separately calculated and then the mean values were ranked from the highest to the lowest priorities (i.e. the highest mean value was the top priority). The Kendall Coefficient of Concordance (W) was calculated to measure the extent of the participants’ agreement to prioritise the impact and the barriers to achieve key technologies. The interpretation used was as follows: coefficient less than 0.1 = very low agreement; coefficient between 0.1 and 0.3 = low agreement; coefficient between 0.3 and 0.5 = average agreement; coefficient between 0.5 and 0.7 = high agreement; and coefficient between 0.7 and 0.9 = very high agreement (Schmidt, 1997).

Results

Demographic characteristics

The number of the participants in the first and in the second rounds of the Delphi study was 39 and 24, respectively (see Table 1). Just over half of the participants were male, and in both rounds, almost half were aged 40–49 years.

Table 1.

Participants’ characteristics: First and second rounds of Delphi study.

Delphi rounds Variables		First round		Second round
Delphi rounds Variables		n	%	n	%
Sex	Male	21	53.8	14	58.0
Sex	Female	18	46.2	10	42.0
Age (years)	30–39	15	38.5	9	37.5
	40–49	17	43.6	11	45.8
	50–59	7	17.9	4	16.7
Education	PhD	38	97.4	23	95.8
Education	GP	1	2.6	1	4.2
Work experience in HIT (years)	1–5	10	25.6	6	25.0
	6–10	13	33.3	9	37.5
	11–16	9	23.1	5	20.8
	More than 16	7	17.9	4	16.7

GP: general practitioners; HIT: health information technology.

Relative importance of key HITs

The 16 key HITs represented in Figure 1 were found to be important for Iran in the future, in both phase 2 (Hemmet et al., 2018) and this phase 3 (Delphi study) of the overall exploration of future HIT needs.

Figure 1.

The mind map of Key HITs for Iran.

Among the key technologies, the highest mean value was related to the implementation of a national health information network (NHIN) with paying adequate attention to the security and privacy issues (4.85 ± 0.54) and the development of necessary infrastructure for NHIN (4.85 ± 0.36) (Table 2). The lowest mean value belonged to the development of clinical decision support systems (4.08 ± 0.83) (Table 2).

Table 2.

Importance of key HITs: Participants’ views.^a

Level of importance KeyHITs	Very important, n (%)	Important, n (%)	Moderately important, n (%)	Less important, n (%)	Mean ± SD	IQR	Q1^b, Q3^c
NHIN	36 (92.3)	0	3 (7.7)	0	4.85 ± 0.54	5 (0)	5, 5
Electronic health records	22 (56.4)	17 (43.6)	0	0	4.56 ± 0.50	5 (1)	5, 4
National cloud-based service centre	22 (56.4)	11 (28.2)	6 (15.4)	0	4.41 ± 0.75	5 (1)	5, 4
Personal health records	19 (48.7)	14 (35.9)	4 (10.3)	2 (5.1)	4.28 ± 0.85	4 (1)	5, 4
Interoperability standards for electronic data exchange	24 (61.5)	12 (30.8)	2 (5.1)	1 (2.6)	4.51 ± 0.72	5 (1)	5, 4
Infrastructure for information sharing	25 (64.1)	11 (28.2)	3 (7.7)	0	4.56 ± 0.64	5 (1)	5, 4
Telemonitoring technologies	18 (46.2)	21 (53.8)	0	0	4.46 ± 0.50	4 (1)	5, 4
Large-scale remote health services	13 (33.3)	17 (43.6)	9 (23.1)	0	4.10 ± 0.75	4 (1)	5, 4
m-Health and its related technologies	27 (69.2)	9 (23.1)	3 (7.7)	0	4.62 ± 0.63	5 (1)	5, 4
Clinical decision support systems	12 (30.8)	21 (53.8)	3 (7.7)	3 (7.7)	4.08 ± 0.83	4 (1)	5, 4
Social networks in healthcare environment	17 (43.6)	14 (35.9)	6 (15.4)	2 (5.1)	4.18 ± 0.88	5 (2)	5, 4
Electronic health insurance system	24 (61.5)	9 (23.1)	6 (15.4)	0	4.46 ± 0.75	5 (1)	5, 4
Business intelligence	19 (48.7)	20 (51.3)	0	0	4.49 ± 0.50	4 (1)	5, 4
Integrated electronic monitoring system	21 (53.8)	12 (30.8)	6 (15.4)	0	4.38 ± 0.74	5 (1)	5, 4
Infrastructure for NHIN	33 (84.2)	6 (15.4)	0	0	4.85 ± 0.36	5 (0)	5, 5
Infrastructure for m-health	27 (69.2)	9 (23.1)	3 (7.7)	0	4.54 ± 0.85	5 (1)	5, 4

HIT: health information technology; IQR: interquartile range; NHIN: national health information network; SD: standard deviation.

^a None of the key technologies was found unimportant; therefore, the related column was removed.

^b The first quartile is equal to the 25th percentile of the data.

^c The third quartile is equal to the 75th percentile of the data.

The expected time of key technologies realisation

The results for the expected time of key HITs realisation are shown in Table 3. Consensus was reached for 11 key technologies in the first round, and the remaining five were examined again the second round of the Delphi study to reach consensus. The majority of the participants (n = 30, 76.9%) predicted that achieving an integrated electronic system to monitor and evaluate healthcare services will be realised between 2016 and 2020. The development of personal health records (n = 32, 82.0%), the development of clinical decision-making systems (n = 30, 76.9%) and the use of business intelligence for collecting and analysing clinical and financial data (n = 32, 82.0%) were predicted to occur after 2025.

Table 3.

Expected time frame for key HIT realisation: Participants’ views.

Expected timeKeyHITs	2016–2020, n (%)	2021–2025, n (%)	After 2025, n (%)	Mean ± SD	IQR	Q1^a, Q3^b	Consensus
NHIN	6 (15.4)	30 (76.9)	3 (7.7)	1.92 ± 0.48	2 (0)	2, 2	✓
Electronic health records	7 (17.9)	20 (51.3)	12 (30.8)	2.13 ± 0.69	2 (1)	2, 2	✗^c
National cloud-based service centre	2 (5.1)	31 (79.5)	6 (15.4)	2.10 ± 0.44	2 (0)	2, 2	✓
Personal health records	3 (7.7)	4 (10.3)	32 (82.0)	2.74 ± 0.59	3 (0)	3, 3	✓
Interoperability standards for electronic data exchange	13 (33.3)	23 (59.0)	3 (7.7)	1.74 ± 0.59	2 (1)	2, 1	✗
Infrastructure for information sharing	4 (10.3)	32 (82.0)	3 (7.7)	1.97 ± 0.42	2 (0)	2, 2	✓
Telemonitoring technologies	3 (7.7)	33 (84.3)	3 (7.7)	2.00 ± 0.39	2 (0)	2, 2	✓
Large-scale remote health services	3 (7.7)	31 (79.5)	5 (12.8)	2.05 ± 0.45	2 (0)	2, 2	✓
m-Health and its related technologies	7 (17.9)	27 (69.2)	5 (12.8)	1.95 ± 0.56	2 (0)	2, 2	✗
Clinical decision support systems	3 (7.7)	6 (15.4)	30 (76.9)	2.69 ± 0.61	3 (0)	3, 3	✓
Social networks in healthcare environment	13 (33.3)	21 (53.9)	5 (12.8)	1.79 ± 0.65	2 (1)	2, 2	✗
Electronic health insurance system	6 (15.4)	30 (76.9)	3 (7.7)	1.92 ± 0.48	2 (0)	2, 2	✓
Business intelligence	3 (7.7)	4 (10.3)	32 (82.0)	2.74 ± 0.59	3 (0)	3, 3	✓
Integrated electronic monitoring system	30 (76.9)	7 (17.9)	2 (5.1)	1.28 ± 0.56	1 (0)	1, 1	✓
Infrastructure for NHIN	15 (38.4)	21 (53.9)	3 (7.7)	1.69 ± 0.61	2 (1)	2, 1	✗
Infrastructure for mhealth	4 (10.3)	32 (82.0)	3 (7.7)	1.97 ± 0.42	2 (0)	2, 2	✓

HIT: health information technology; IQR: interquartile range; NHIN: national health information network; SD: standard deviation.

^a The first quartile is equal to the 25th percentile of the data.

^b The third quartile is equal to the 75th percentile of the data.

^c ✗: Consensus was not reached in the first round of the Delphi study.

Key impact of HITs

The results showed that in most cases, there was agreement among the experts in ranking the impact of key HITs in both Delphi rounds (Table 4). The highest agreement was related to implementing the NHIN with the greatest impact on improving communications between healthcare organisations. For this technology, the experts gave the lowest impact ranking to ‘improving the quality of life’. According to the Kendall’s coefficient of concordance, the agreement between the participants in the first round was higher than 0.7 and in the second round, it was between 0.5 and 0.7, indicating a very high level of agreement. The lowest Kendall’s coefficient of concordance was related to the impact of developing basic infrastructure for m-health. The agreement of the participants in the first round was 0.286 and in the second round was 0.203, indicating a low level of agreement. For this technology, the experts gave the highest and the lowest impact ranking to ‘Facilitating patient–provider communication’ and ‘saving cost’, respectively.

Table 4.

Impact of key HITs: Participants’ views.

Impact Key Delphi rounds HITs	Saving cost		Increasing productivity		Facilitating patient–provider communication		Improving communications between healthcare organizations		Improving healthcare quality		Improving quality of life		Kendall (W), p < 0.001
Impact Key Delphi rounds HITs	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	First round	Second round
NHIN	3	3	5	5	2	2	1	1	4	4	6	6	0.738	0.686
Electronic health records	4	4	3	3	2	2	5	5	1	1	6	6	0.441	0.447
National cloud-based service centre	1	1	2	2	4	3	3	4	5	5	6	6	0.675	0.672
Personal health records	2	2	5	5	3	3	6	6	1	1	4	4	0.379	0.334
Interoperability standards for electronic data exchange	4	4	5	5	2	2	1	1	3	3	6	6	0.498	0.410
Infrastructure for information sharing	4	5	3	3	2	2	1	1	5	4	6	6	0.661	0.539
Telemonitoring technologies	2	1	1	2	5	5	6	6	3	3	4	4	0.299	0.317
Large-scale remote health services	3	3	5	5	1	1	4	4	2	2	6	6	0.532	0.388
m-Health and its related technologies	2	2	4	4	3	3	6	6	1	1	5	5	0.318	0.252
Clinical decision support systems	4	4	2	2	5	5	6	6	1	1	3	3	0.407	0.395
Social networks in healthcare environment	6	6	2	2	1	1	5	5	3	4	4	3	0.550	0.430
Electronic health insurance system	2	3	1	1	5	5	3	2	4	4	6	6	0.359	0.307
Business intelligence	3	4	1	1	6	6	5	5	2	2	4	3	0.502	0.453
Integrated electronic monitoring system	2	2	1	1	5	5	6	6	3	3	4	4	0.573	0.516
Infrastructure for NHIN	5	6	4	4	1	1	2	2	3	3	6	5	0.642	0.663
Infrastructure for m-health	6	6	2	2	1	1	4	4	3	3	5	5	0.286	0.203

HIT: health information technology; NHIN: national health information network. The level of significance was α = 0.05.

Obstacles to achieve key HITs

The results showed that in most cases, there was agreement in ranking barriers to achieve key HITs in the both Delphi rounds (Table 5). The highest level of agreement was related to the development of basic infrastructure for m-health and its barriers (first round = 0.656, second round = 0.667). According to Kendall’s coefficient of concordance, the level of agreement between 0.5 and 0.7 indicates a high level of agreement. For this technology, the experts gave the highest and the lowest barrier ranking to ‘limited financial resources’ and ‘lack of user acceptance’, respectively. The lowest level of agreement was related to the barriers ahead of developing infrastructure for information sharing (first round = 0.376, second round = 0.367), and the level of agreement between 0.3 and 0.5 indicates an average level of agreement. The experts gave the highest and the lowest barrier rankings to ‘lack of stakeholders’ involvement’ and ‘weakness in organisational culture’, respectively.

Table 5.

Barriers to achieving key HITs: Participants’ views.

Barriers Key Delphi rounds HITs	Limited financial resources		Weakness in planning		Lack of user acceptance		Lack of stakeholders’ involvement		Weakness in organizational culture		Lack of the laws about using IS/IT		Kendall (W), p < 0.001
Barriers Key Delphi rounds HITs	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second Round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	Rank in the first round	Rank in the second round	First round	Second round
NHIN	1	1	2	2	5	5	6	6	4	4	3	3	0.627	0.553
Electronic health records	2	2	1	1	3	3	4	4	6	6	5	5	0.576	0.544
National cloud-based service centre	1	2	2	1	6	6	3	3	5	5	4	4	0.624	0.525
Personal health records	5	5	1	1	4	4	6	6	2	2	3	3	0.558	0.378
Interoperability standards for electronic data exchange	1	1	2	2	5	5	4	3	6	6	3	4	0.582	0.551
Infrastructure for information sharing	3	3	4	4	2	2	1	1	6	6	5	5	0.376	0.367
Telemonitoring technologies	2	2	1	1	6	6	3	3	4	4	5	5	0.611	0.482
Large-scale remote health services	1	1	2	2	6	6	3	3	4	4	5	5	0.546	0.700
m-Health and its related technologies	3	4	1	1	2	2	6	6	5	5	4	3	0.543	0.492
Clinical decision support systems	2	2	1	1	3	3	4	4	6	6	5	5	0.422	0.39
Social networks in healthcare environment	4	4	3	3	5	5	6	6	1	1	2	2	0.628	0.617
Electronic health insurance system	4	4	1	1	6	6	3	3	2	2	5	5	0.487	0.420
Business intelligence	2	2	1	1	6	6	3	3	4	4	5	5	0.488	0.458
Integrated electronic monitoring system	4	4	1	1	6	6	2	2	3	3	5	5	0.491	0.345
Infrastructure for NHIN	1	1	2	2	6	6	5	5	4	4	3	3	0.674	0.602
Infrastructure for m-health	1	1	2	2	6	6	4	5	5	4	3	3	0.656	0.667

HIT: health information technology; IS: information system; IT: information technology; NHIN: national health information network.

Discussion

The present study aimed to gain experts’ opinions on the relative importance of key HITs, expected time of realisation, areas that the technologies may impact and the obstacles to achieve them in Iran until 2025. To the best of our knowledge, this is the first time a predictive study of HIT needs for Iran has been conducted.

It is notable that the importance of these HITs has been highlighted in other countries. For example, several studies have highlighted the importance of implementing an NHIN (Carroll, 2007; Dobalian et al., 2012; Kuperman et al., 2010; Valle et al., 2016). In many countries, the implementation of the EHRs has been recognised as a priority among other HITs (Buntin et al., 2011; Doberne et al., 2017; Martinez-Costa et al., 2014; Weinfeld and Mishori, 2017), and in various studies, the importance of using personal health records has been emphasised (Kang et al., 2015; Luo et al., 2012; Toscos et al., 2016). The development of a national data centre (Ahlqvist et al., 2007; Weng et al., 2016), interactive standards for HIT (Chronaki et al., 2014; Mookencherry, 2012; Mccarthy, 2016), information sharing between health information systems at a national level (Alexander et al., 2016; Vest and Abramson, 2015; Zaidan et al., 2015), telehealth (Dinesen et al., 2016; Tuerk et al., 2010; Teot, 2014) and developing m-health (Berrouiguet et al., 2016; Goyal and Cafazzo, 2013; Sikka and Barash, 2012) were among the technologies that have been discussed in other studies.

Similarly, other key technologies, such as telemedicine (Bagayoko et al., 2011; Thiede and McIntyre, 2008; Wade, 2013), clinical decision support systems (Collins et al., 2012; Rothman et al., 2012; Sen et al., 2012), business intelligence in the field of healthcare (Ashrafi et al., 2014; Bonney, 2013; Corrales, 2010), electronic monitoring and evaluation system for health technology (Myburgh et al., 2015; Shuren and Califf, 2016) and developing a necessary infrastructure for NHIN (Capozzi and Lanzola, 2013; Gerrity, 2013; Li and Wilson, 2013), have been regarded as key technologies by other researchers. Overall, it seems that the findings of the current study are in line with the results of other similar studies, and the 16 mentioned key HITs are also important for other countries. Therefore, one can conclude that the experiences of other countries can provide valuable lessons for the future development of HITs in Iran.

In relation to the expected time of key technology realisation in Iran, the results showed that most key technologies were expected to be realised between 2021 and 2025. Likewise, in other studies, the future of HIT has been discussed in a similar time frame. In a European foresight study, the implementation of an integrated health information network was highlighted and predicted to be realised between 2010 and 2020 (Compano et al., 2006). In the United States, connecting physicians, hospitals and patients to the health information network was set by 2019 (Valle et al., 2016). In Iran, the implementation of the health information network was initially introduced in the comprehensive scientific health plan, but has not yet been realised (Riazi and Abedian, 2011). In a foresight study conducted in Japan, it was anticipated that the implementation of the EHRs would be completed by 2023 (Kuzuno et al., 2008). In the United States, identifying, prioritising and progressing in the formulation and implementation of technical standards to support health information exchange were set to be achieved by 2020 (The Office of the National Coordinator for Health Information Technology (ONC), 2014). Moreover, a program was initiated to promote health information exchange at the national level and to improve the quality of healthcare by 2018 (DeSalvo, 2016; Ridic et al., 2012). In South Korea, an electronic health information exchange project was launched in 2005, and despite being stopped at some levels in 2010, it has been relaunched since 2015 to reach the target. It seems that setting a time frame for technology realisation depends on many factors, such as constraints and priorities in each country. The current technical infrastructure and information systems in each country are also important to predict how far-reaching a new technology is. Therefore, with respect to the current policies and movement in the field of HIT in Iran, it seems that experts’ views of expected time of key technologies realisation in this study are realistic.

Regarding the impact of key HITs, the results showed that the highest ranked impact for most technologies was facilitating patient–provider communication (as a result of implementing large-scale remote health services, social networks in healthcare environment, infrastructure for NHIN and infrastructure for m-health) and improving healthcare quality (as a result of implementing EHRs, personal health records, m-health and its related technologies and clinical decision support systems). Improving communications between healthcare organisations could be realised by implementing a NHIN, developing interoperability standards for electronic data exchange across a variety of health applications and developing an infrastructure for information sharing across the public and private healthcare organisations. Increasing productivity could be the main impact of developing an electronic insurance system, implementing business intelligence in collecting and analysing clinical and financial data and implementing an integrated electronic system to monitor and evaluate healthcare services. Finally, saving cost was ranked as the first ranked impact of developing a national cloud-based service centre for integrating health data. The impact of HIT has been discussed in various studies. For example, Compano et al. (2006) found that the most important impact of implementing a health information network was facilitating the simultaneous communication between different centres, while Kuperman et al. (2010) found that increased communication between healthcare organisations was the main impact of this network. In another study, improving the quality of healthcare, increasing communication and saving costs were highlighted as the most important variables to impact the implementation of a health information network and the most important obstacle was the lack of financial resources (Valle et al., 2016). Regarding the impact of implementing EHRs, improving the quality of healthcare was recognised as the most important impact (Bishop et al., 2015; Buntin et al., 2011; Keyhani et al., 2008). The main impact of implementing a national health data centre was improving management, efficiency, speed and the quality of healthcare as well as reducing costs (Löhr et al., 2010; Seddon and Currie, 2013; Weng et al., 2016). In other studies, the most important impact of developing personal health records was found to be saving costs, improving quality of healthcare (Abdolkhani et al., 2014; Ford et al., 2016) and increasing efficiency (Zhou et al., 2015). As the results show, each technology may have a different impact and it is important for the policymakers to know these details to ensure they plan for outcomes that they really want in the health sector. The health status and the trends across multiple diseases and conditions are helpful to decide which technology is more beneficial to improve the health status of a country.

Regarding the barriers to achieve key HITs, weakness in planning was the highest ranked barrier for nine key technologies and financial limitation was a major obstacle to achieve five key technologies. The lack of stakeholders’ participation in developing infrastructure for information sharing and weakness in organisational culture for using social networks in healthcare environment were the first ranked barriers to achieve these technologies. Overall, it seems that the research findings are in line with the results reported by other studies. Lenert et al. (2012) reported that the main obstacle to implement a health information network in the United States was political factors that need to be overcome by creating a common vision among the stakeholders. Regarding the barriers ahead of acceptance and deployment of EHRs, financial and technical issues were found to be the most important obstacles (Ayatollahi et al., 2014; Kruse et al., 2016). In another study, Seddon and Currie (2013) found that the most important barrier to develop a national health data centre was the lack of stakeholders’ participation in the process of technology development and deployment. According to Pinciroli and Pagliari (2015), patient acceptance of technology was a barrier to the development of personal health records. In another study, financial resources constraints were the most important obstacles to develop the interoperability standards for exchanging electronic health information (Chronaki et al., 2014). Similarly, the lack of clinicians’ and patients’ acceptance (Haluza and Jungwirth, 2014) as well as the lack of financial resources (Dinesen et al., 2016) were reported as the most important obstacles to develop m-health services. It seems that most of the barriers reported in the current study are similar to those ones that have been reported in other studies. However, a weakness in planning was found to be the first ranked barrier to achieve most key technologies in Iran. As investment in information technology requires having adequate knowledge about context, technology and users, there are many technical and non-technical obstacles to overcome. As many projects and plans may fail due to the lack of adequate knowledge and experience, it seems that the results of the current study and similar ones can help policymakers make better decisions for the future of HIT in Iran.

Overall, the results of the current study can be used by policymakers as a road map for planning for the future of HITs in Iran. The expected time frame for the technology realisation, the impact on and the barriers to achieve each technology can help policymakers be more realistic in strategic, tactical and operational planning. Moreover, any investment in the future HITs may lead to projects that are really necessary for Iran.

Research limitations

The main limitation of this study relates to the limited number of participants in the first and particularly the second round of the questionnaire. However, the results of the Delphi study were consistent with the results of the previous qualitative study (Hemmat et al., 2018), and it appears that the limited number of the participants did not affect the results.

Conclusion

Our study identified for Iran the relative importance of key HITs, the expected time of technology realisation, the areas that the technologies may impact and the obstacles to achieve them. Most key technologies were expected to be realised between 2021 and 2025. The impact of key technologies was different for each technology, and the first ranked barrier to achieve 9 of the 16 key technologies was weakness in planning. By determining key HITs and predicting the time of technology achievement, health policymakers are better equipped to formulate strategic and operational plans. This information may also help policymakers make better and more realistic decisions. Moreover, understanding the overall impact and barriers to implementation will assist strategic planning processes to be more accurate. Further research is needed to explore each technology in more detail and to evaluate its level of achievements over time.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded and supported by Iran University of Medical Sciences (IUMS/SHMIS_93/115).

ORCID iD

Haleh Ayatollahi, PhD

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