Patient preferences for health information technologies: a systematic review
Original Article

Patient preferences for health information technologies: a systematic review

Norah L. Crossnohere, Brent Weiss, Sarah Hyman, John F. P. Bridges

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

Contributions: (I) Conception and design: JFP Bridges, B Weiss, S Hyman; (II) Administrative support: B Weiss, S Hyman; (III) Provision of study materials or patients: None; (IV) Collection and assembly of data: JFP Bridges, B Weiss, S Hyman; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

^ORCID: 0000-0002-2811-1330

Correspondence to: Norah L. Crossnohere, MHS, PhD. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. Email:

Background: Advances in patient-facing health information technology (HIT) promise to improve health care delivery and patient outcomes. Low utilization of HIT suggests that the preferences of patients may not be adequately guiding the development of these technologies. This systematic review offers an assessment of published evidence regarding patient preferences for HIT.

Methods: Articles addressing preferences for HIT from patient and other end-user groups published up through 2020 were identified from PubMed, Web of Science, Scopus and via hand searching. Articles that used quantitative stated-preference methods to explore preferences for HIT were eligible for inclusion. Studies that explored attitudes towards HIT without eliciting trade-offs were excluded. Critical appraisal of study quality was conducted using the PREFS checklist and quality criteria identified by the US Food and Drug Administration including heterogeneity analysis and patient engagement in study design. We conducted thematic analysis of the main preference findings from each study to synthesize patient and end-user preferences for HIT. The review was not registered and authors received no funding to conduct the review.

Results: The search yielded 7,299 unique articles, 59 of which were ultimately included in the review. Studies explored preferences for telemedicine (n=30), patient portals (n=12), mHealth (n=9) or multiple HITs (n=8). Preference elicitation methods included direct elicitation (n=26), discrete-choice experiments (n=13), conjoint analysis (n=6), contingent valuation (n=5), and ranking exercises (n=9). Studies had a mean PREFS score of 3.51 out of 5. Forty-two studies conducted preference heterogeneity analysis and only 20 included patients in study design. Thematic meta-analysis indicated that patients prefer HIT that is convenient and lower cost, but does not sacrifice quality, and preferences varied by demographic features such as age as well as depending on the type of health information being communicated.

Conclusions: Patient and end-users have distinct preferences for the use of HIT in their medical care. It is timely that researchers and healthcare administrators consider these preferences for HIT given its rapid uptake amidst the COVID-19 pandemic. Although this literature demonstrates that patients can be engaged as participants in preference studies to identify meaningful aspects of HIT, the field was limited in its inclusion of patients in the design of such studies. Future development of HIT should be guided by high-quality preference research that integrates patients in all stages in the design and implementation of HIT.

Keywords: Telehealth; mHealth; patient preferences; end-users

Received: 10 July 2020; Accepted: 20 November 2020; Published: 25 September 2021.

doi: 10.21037/jhmhp-20-105


Health information technology (HIT) has become central to the provision of healthcare (1). HIT broadly encompasses the use of electronic hardware to address the storage, retrieval, and sharing of health information to inform communication and decision making (2). The Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 created incentives for the use of HIT services such as electronic medical records, and widened the scope of privacy and security protections for electronic health data (3). As systems capable of storing, analyzing, visualizing, and communicating data to patients and providers, HIT can facilitate patient reminders, support diagnostics, gather and synthesizing important medical information, and facilitate evidence-based decision making at the point of care (4). In clinical settings, HIT applications range from simple digital charting and clinical e-mail to the integration of advanced decision support tools into virtual patient portals (5). The uptake of HIT has allowed health care providers and patients to securely share health information and more efficiently coordinate care and manage the receipt of health services (6). Data generated from HIT can also inform regulatory decision making given that the 21st Century Cures Act has prompted the inclusion of real-world evidence in product review (7).

There is general agreement that HIT has potential to improve healthcare quality and patient outcomes. A recent systematic review found that over 80% of studies integrating HIT resulted in at least one improved medical outcome among patients (8). Appropriate use of HIT has been demonstrated to reduce human and medical errors (9), improve comprehensive care coordination, monitoring and surveilling patient data over time, and improve clinical health outcomes (5). HIT also has the potential to improve outcomes for providers and health systems, such as through streamlining clinical workflow (10) and reducing health care costs (11). HIT is also thought to increase access to care (12).

Despite the promise of HIT to improve the quality of healthcare it continues to face satisfaction and implementation barriers hindering its success (13,14). A US study indicated that poor system functionalities, difficulty using, and hardware issues reduced clinician satisfaction with the use of HIT such as electronic health records (EHR) (15). A systematic review of problems with HIT spanning studies in six countries found that problems with HIT included issues with functionality, poor user interfaces, fragmented displays, and challenges in accessing the system (16). Patients and clinicians have expressed concerns that the use of technology hinders rapport-building (17), although other reports indicate that HIT can improve doctor-patient relationships by automating workflows and increasing clinician-patient time (10). A systematic review in primary care across seven countries found that neither quality of care, patient safety nor provider/patient relationships were affected by the adoption of EHR, but that implementation success was fostered by insulating features within the health system such as strong leadership, project management, standardization, and training (18). This indicates that successful implementation of HIT may depend not just on the effectiveness of the technologies themselves but on the contexts in which they are applied.

Patient-centeredness involves providing care that is concordant with patients’ needs and values and respectful of/responsive to patient preferences (19). Health informatics have the potential to facilitate patient-centered care and the field has evolved to consider technology’s role in the acquisition, storage, and usage of health care data (20). Optimized HIT may increase patient satisfaction and perceived satisfaction with and quality of care, as well as improve health outcomes HIT (7,21). Conversely HIT may also detract from the patient-centeredness of care when applied inappropriately (22). Ensuring that HIT systems are aligned with and responsive to patients’ preferences, needs and values is essential to making them patient-centered. Doing so is a priority area for the Agency for Healthcare Research and Quality (23).

Methods to measure the preferences of patients have been rigorously applied to explore the preferences of patients in diverse healthcare settings (24,25). Stated-preference methods are a well-established and rapidly growing suite of preference elicitation approaches with application in clinical, policy, and regulatory decision making (26-28). Stated-preference methods can help in identifying what attributes of a given health service individuals value most and what tradeoffs they are willing to make to achieve them. Understanding patient preferences for HIT can help in the development of HIT systems that are acceptable to patients. They can also ensure that HIT is used to support and enhance patient’s interactions with healthcare systems rather than detract from them.

This systematic review characterizes how stated-preference methods have been used to explore patient and other end-user preferences for HIT. Previous research has systematically reviewed other processes measures associated with HIT including its adaption (16,29,30), satisfaction and attitudes (7,31,32), and barriers and facilitators (33,34). In addition to providing substantial information regarding patient and other end-user preferences for HITs, we also offer methodological recommendations on how to evaluate the quality and bias in patient preference studies following good research practices, and how to synthesize substantive information about patient preferences using meta-synthesis. We present the following article in accordance with the PRISMA reporting checklist (available at


Key questions

We conducted a systematic review and meta-synthesis to explore the use of preference-elicitation methods in evaluating HIT. The review was guided by three overarching questions: (I) In what HIT and healthcare contexts are preference studies being conducted? (II) What is the typology and quality of HIT preference studies? (III) What are patients’ and other end-users’ preference for HIT? Answers to these questions will make an important contribution to the literature by providing information that can be used to inform the development, application, and evaluation of HIT from a patient-centered perspective. This review follows protocols based on other systematic reviews of preference-elicitation methods (35,36).

Inclusion and exclusion criteria

Studies were eligible for inclusion if they (I) discussed HIT; (II) used a quantitative trade-off based stated-preference method, including: direct-elicitation, discrete-choice experiments, conjoint analysis, and ranking; (III) elicited the preferences of patients, caregivers, or end-users; (IV) were available in English, and (V) were full-text documents. Studies that only assessed the preferences of health care providers for HIT were excluded. Studies in which the preference-elicitation approach did not involve a trade-off, such as “select all that apply” questions or Likert-type rating, were excluded. Abstracts and purely qualitative studies were excluded. For this review HIT was defined following Brailer et al.’s description of HIT as “the application of information processing involving both computer hardware and software that deals with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision making” (2). Studies describing such technologies were eligible for inclusion, prominent examples of which include EHR, patient-portals, and telehealth. Two reviewers assessed studies at the title/abstract and full-text stages to determine inclusion (BW, SH). Conflicts were addressed by a third reviewer (JB).

Search strategy

The search strategy included three concepts: (I) HIT, (II) stated-preference methods, (III) patients, caregivers, and end-users. We performed a preliminary search of PubMed and Web of Science in January 2020. The search strategy was amended, and run in PubMed, Web of Science, and Scopus in April 2020. The final search terms are presented in Table S1. This search was supplemented with additional hand-searching of the reference lists of all included studies, and the journals Journal of Telemedicine and e-Health, Journal of Telemedicine and Telecare, Journal of American Medical Informatics, BMC Medical Informatics and Decision Making, Journal of Medical Internet Research from 2004 to 2020. These journals were selected for additional hand-searching because they had produced many relevant/nearly relevant hits in database searches.

Data abstraction & critical appraisal

The following categories of information were extracted: author, date, country, preference-elicitation method, sample size, HIT context (i.e., telemedicine, patient portal, mHealth, or multiple HITs), and healthcare context (i.e., receiving healthcare, managing health data, receiving health data, or multiple healthcare contexts), and key preference finding.

We assessed study quality using the PREFS checklist (35) which evaluates the quality of reporting of stated-preference studies according to five criteria: purpose of study, respondent sampling, explanation of assessment methods, findings, significance testing. Studies achieved a score of 1 for every criterion that meet PREFS standards, and a 0 otherwise. The range of possible PREFS scores is 0–5. ANOVA tests were used to explore differences in PREFS scores across preference-elicitation and HIT contexts. We also assessed study quality according to several criteria posed by the US Food and Drug Administrations for patient preference studies, including: justification of sample size, heterogeneity considerations, relevance/comprehension to the patient population (37,38).

General data abstraction was split between two reviewers (BW, SH). PREFS scores and FDA recommended qualities were independently assigned by two reviewers (BW, SH) and discrepancies were resolved through consensus agreement between the reviewers.

Meta-synthesis using thematic analysis

Key preference findings abstracted from all studies were synthesized using thematic analysis. Thematic analysis is a foundational approach in qualitative analysis and is used for identifying, analyzing and characterizing repeated topics and ideas (39,40). The key preference finding was thematically analyzed by three reviewers (NC, BW, SH). The three reviewers independently identified themes, and then collectively revised and refined themes. Two reviewers conducted a final thematic categorization of the key findings from each included article (BW, SH) and a third reviewer (NC) reconciled any conflicts.

The review was not registered and authors received no funding to conduct this review. The review protocol can be accessed from authors upon reasonable request.


Identified studies

The search strategy identified 9,152 results via systematic review methods and 14 results via hand searching as is visualized in the PRISMA diagram presented in Figure 1. After removing 1,867 duplicates, titles/abstracts of 7,299 papers were reviewed. A full-text review was conducted for 130 articles, 71 of which were excluded as they did not meet all inclusion criteria. In total 59 articles met all criteria and were included in the review. Several studies met many but not all criteria. For instance, some studies assessed preferences of clinicians rather than patient/caregiver end-users (41,42), and others used qualitative methods rather than quantitative trade-off techniques to describe preferences for HIT (43).

Figure 1 PRISMA flow chart of study identification and selection

Characteristics of included studies

Table 1 describes the characteristics of included studies. Most studies were conducted in the US (n=29), followed by Australia (n=9), the Netherlands (n=4), South Korea (n=3), Germany (n=3), United Kingdom (n=2), Canada (n=2 each), Italy (n=2) and Israel, Turkey, Sweden, and Japan (n=1 each). Only one study spanned multiple countries (44). The majority of studies were published from 2010 to 2020 and this growth of preference studies for HIT is visualized in Figure 2. Study sample sizes ranged from 34 to 20,882 participants. Studies using an experimental preference method (i.e., DCE, conjoint analysis, contingent valuation) had the highest average sample size (average n=1,640) followed by direct elicitation (average n=421) and ranking (average n=178). Data extracted from included studies is available from authors upon reasonable request.

Table 1

Characteristics of included HIT preference studies

1st author, year Country Method N HIT context Healthcare context PREFS
Brick, 1997 USA DE 461 Telemedicine Telemedicine services for rural populationsa 3 EFS
Lowitt, 1998 USA DE 131 Telemedicine Teledermatolgy examination of veteransa 2 EF
Tsuji, 2003 JAP CV 291 Telemedicine WTP for telemedicine servicesa 3 PES
Dick, 2004 CAN VAS 57 Telemedicine Care support following hospital dischargea 3 PES
Hassol, 2004 USA DE 1,421 EHR EHR web-based communicationb 3 PEF
Bradford, 2004 USA CV 126 Telemedicine WTP for CHF telemedicine servicesa 4 PEFS
Bradford, 2005 USA CV 366 Telemedicine WTP for CHF and hypertension telemedicine servicesa 3 PES
Qureshi, 2007 USA CV 92 Telemedicine WTP for telemedicine servicesa 3 PEF
Mofid, 2007 USA DE 98 Telemedicine Teledermatology vs face-to-face consultationa 4 PEFS
Basoglu, 2010 TUR CA 161 Telemedicine Remote clinical servicea 3 PEF
Park, 2011 SKR CA 118 Telemedicine Diabetes Management Servicea 3 PES
Basu, 2011 USA DE 129 mHealth Receiving imaging resultsc 4 PEFS
Vandelanotte, 2011 AUS DE 803 Telemedicine Physical activity interventiona 4 PEFS
Johnson, 2012 USA DE 53 OPP Receiving radiological reportsc 3 PEF
Ranney, 2012 USA DE 664 mHealth, Telemed Technology-based behavioral Interventionsa 5 PREFS
Jung, 2012 SKR DE 243 Telemedicine General telemedicine servicesa 3 PES
Grande, 2013 USA CA 3,064 OPP Secondary uses of health datab 4 PRES
Ahn, 2014 SKR CA 400 Telemedicine General telemedicine servicesa 4 PEFS
Quinlivan, 2014 AUS DE 474 PCEHR Health record storage systemb 4 PEFS
Muench, 2014 USA DE 277 mHealth Messaging for behavioral interventionsa,b 3 PES
Stephen, 2014 UK CV 34 Telemedicine WTP for dementia telecare servicesa 4 PEFS
Stypulkowski, 2015 USA DE 346 Telemedicine Surgery postoperative follow-upa 3 PEF
Lal, 2015 CAN DE 67 Telemedicine Receiving mental health services and informationa 3 PEF
Choudhry, 2015 USA RE 301 mHealth, OPP Receiving skin biopsy resultsc 4 PEFS
Cabarrus, 2015 USA DE 617 mHealth, OPP Receiving radiological reportsc 4 PEFS
Cabitza, 2015 IT RE 385 PHR PHR functionalitiesb 4 PEFS
Kaambwa, 2016 AUS DCE 330 Telemedicine Health care services for older peoplea 4 PEFS
Wallin, 2016 SWE DE 343 Telemedicine Internet based psychological Interventionsa 3 PES
Determann, 2016 NET DCE 1,443 PHR Managing health data access, sharing, and storageb 4 PEFS
Patil, 2016 EU DCE 20,882 OPP Managing health data access, sharing, and storageb 3 PES
White, 2016 UK RE 201 EHR EHR functions and access needsb 4 PREF
Ray, 2016 USA DE 439 mhealth Receiving ED discharge informationc 4 PEFS
Spinks, 2016 AUS DCE 35 Telemedicine Teledermoscopy for melanoma screening 4 PEFS
Granger, 2016 AUS DE 1,865 mhealth mhealth intervention & info. deliveryb 4 PEFS
Brazeal, 2017 USA RE 125 mhealth, OPP Breast biopsy result notificationc 4 PEFS
Chang, 2017 USA DCE 5,921 Telemedicine Online health servicesa 5 PREFS
Cranen, 2017 NET DCE 104 Telemedicine Pain rehabilitationa 3 PES
Marchell, 2017 USA RE 201 Telemedicine Teledermatology examination methodsa 3 PES
Andino, 2017 USA VAS 108 Telemedicine Video visits at outpatient urology clinica 2 PE
Boyde, 2018 AUS DCE 200 mHealth, Telemed Delivering cardiac rehabilitation servicesa 4 PEFS
Deidda, 2018 IT DCE 2,000 Telemedicine Cardiology servicesa 3 PES
Snoswell, 2018 AUS DCE 113 Telemedicine Teledermoscopy for skin cancer screeninga 4 PEFS
Saraswathula, 2018 USA DE 107 OPP Communication of biopsy resultsc 4 PEFS
Nayyar, 2018 USA CA 774 mHealth Aesthetic surgery informationc 3 PEF
Wildenbos, 2018 NET DCE 1,294 OPP Patient portal functionalitiesb 3 PES
Russell, 2018 USA RE 46 mHealth Medication self-management app featuresb 2 PF
Apolinario-Hagen, 2018 GER DE 646 mHealth, Telemed Internet based therapiesa,b 2 PE
Cronin, 2018 USA DE 480 OPP Online PROMIS health assessment dashboardb 3 EFS
Offermann-van Heek 2019 GER CA 140 mHealth, Telemed Ambient Assisted Living (ALL) technologiesc 2 PE
Chudner, 2019 IL DCE 508 Telemedicine Video consultations in primary carea 4 PEFS
Nagao, 2019 USA DE 40 Telemedicine Audiometry telehealth servicesa 4 PEFS
Morland, 2019 USA RE 180 Telemedicine PTSD treatment preferencesa 4 PEFS
Woolen, 2019 USA DCE 418 OPP Releasing cancer radiological test resultsc 4 PEFS
Plinsinga, 2019 AUS DE 259 mHealth Osteoarthritis self-management support groupsb 3 PEF
Edwards, 2020 USA DE 112 mHealth, OPP Communication of pediatric radiology resultsc 4 PEFS
Slightam, 2020 USA DE 764 Telemedicine Clinical services for veterans with access barriersa 5 PREFS
Lim, 2020 AUS CA 547 mHealth Digital health administrationb 4 PEFS
Nguyen, 2020 GER DE 65 mHealth Reporting adverse events following immunizationb 3 PEF
Barsom, 2020 NET DE 50 Telemedicine Video follow-up consultations for colorectal cancera 4 PEFS

Healthcare Context Groups: a, receiving healthcare; b, managing health data and healthcare; c, receiving health data. USA, United States; UK, United Kingdom; CAN, Canada; SKR, South Korea; EU, European Union; AUS, Australia; SWE, Sweden; IT, Italy; GER, Germany; NET, Netherlands; IL, Israel; DE, direct elicitation; CV, contingent valuation; VAS, visual analogue scale to facilitate a ranking exercise; EHR, electronic health record; CA, conjoint analysis; OPP, online patient portal; RE, ranking exercise; PHR, personal health record; PCEHR, personally controlled electronic health record; WTP, willingness to pay; DCE, discrete-choice experiment; ED, emergency department; CHF, chronic heart failure.

Figure 2 Growth of HIT preference research over time

Critical appraisal of study quality

Three studies met all five PREFS criteria (45-47), 28 studies met four criteria, 24 met three criteria, and the remaining four studies met two criteria (Table 1). The average PREFS score was 3.51 out of 5 (SD 0.70). Almost all studies met criteria for stating the preference purpose (“P” in PREFS, n=58) and explaining the preference-elicitation methodology (“E”, n=56). Few studies demonstrated that responders were similar to non-responders (“R”, n=5). Three-quarters of studies appropriately included respondents in the findings (“F”, n=43) and used significance tests (“S”, n=45). Average PREFS score did not vary across preference-elicitation (ranking vs. direct elicitation vs. experimental methods; P=0.73) or by HIT context (P=0.69). Inter-rater reliability of study quality using PREFS was 0.80 before a consensus score was assigned for every study, and 1.00 after consensus.

In assessing study quality using criteria outlined by FDA we found that 71% of studies (n=42) conducted heterogeneity analysis. Preference heterogeneity was generally assessed through sub-group analysis of patient demographics, medical conditions, technology familiarity, health literacy, or some other distinguishing characteristic of the research population. About a third of the articles (n=20) engaged patients in the development of the preference elicitation tool through either a focus group, pilot study, or both. Only 15% of studies (n=9) justified their sample size.

Preference elicitation approaches

We segmented literature into three preference-elicitation categories based on the preference study design: (I) experimental preference methods (DCE, conjoint analysis, contingent valuation), (II) direct elicitation, and (III) ranking exercises.

Experimental preference methods

Twenty-four studies used experimental preference elicitation approaches including DCEs (n=12) (44,46,48-56), conjoint analysis (n=7) (57-63), and contingent valuation (n=5) (64-68). Of the 19 studies that used a DCE or conjoint analysis, 14 had a choice-based design (44,46,48-56,61,63,69), three had a rank-based design (57-59), one used a value-based conjoint (60), and one used a take-it-or leave conjoint analysis (62).

In the choice-based designs, multiple choice tasks were presented to respondents, each consisting of two or more profiles described by various attribute levels relevant to the healthcare and HIT context. Seven of the studies using choice-based designs offered an opt-out choice in which the respondent could choose none of the presented profiles (44,50,53,54,56,61,69). For example, Determann et al. included an opt-out option in their DCE that explored EHR preferences in order to make the experiment resemble the real-life situation where respondents are not obligated to have a EHR (53).

The total number of choice tasks presented to respondents in a given preference study using a choice-based design ranged from 5–22 (mean 11.36, SD 4.78). Eight of these studies used blocked-designs wherein a given respondent received a subset of the total choice tasks (44,46,48-50,52,56,69). In these studies, the total number of choice tasks ranged from 12–120. One such study was a pan European survey of online patient portal preferences that analyzed a total of 120 choice tasks through surveys that only presented five choice tasks to each respondent (44). In choice-based design studies, the number of attributes presented per tasks ranged from three to eight (mean 5.43, SD 1.45).

Rank-based conjoint analysis involved respondents ranking multiple profiles described by various attribute levels from most preferred to least preferred. Only three studies used this approach (57-59). Five studies used a contingent valuation approach, including those using open-ended (64,67), bounded (65,66), and both open-ended and bounded (68) methodologies. These five studies all explored willingness to pay for access to telemedicine services.

Direct elicitation

A total of 26 studies including (45,47) and (70-93) used direct elicitation approaches to identify HIT preferences. . The direct elicitation methodologies involved questions in which respondents chose between multiple options related to the HIT context. Twelve studies included an opt-out option wherein participants could choose neither offered option. Examples of direct elicitation type questions included: “What is your preferred platform for delivery of personalized health information?” (87) and “In case of emotional problems, which of the described interventions would you most likely personally use?” (93). Most direct elicitation studies (n=18) used one or two questions to address a particular HIT healthcare context, but eight studies utilized three or more questions. Only one study asked more than seven preference elicitation questions (86).

Ranking exercises

Nine studies used ranking methods to identify preferences for HIT (94-102). The ranking exercises involved questions or tasks in which respondents ranked HIT attributes, such as modalities for receiving biopsy results (101,102) or electronic health records/personal health record functions (98,99), from most important to least important. In three studies, a ranking exercise was paired with another research task (95,97,100). Two studies utilized a visual analogue scale to perform a ranking task (94,96).

HIT and healthcare context

The most studied HIT context was telemedicine (n=30) followed by patient portals including electronic health records (n=12), mHealth (n=9) and multiple HITs (n=8). A matrix visualizing HIT context and the preference-elicitation approaches used to evaluate them is presented in Table 2.

Table 2

HIT typology and preference elicitation method

HIT type Direct elicitation Discrete-choice experiment Conjoint analysis Ranking exercise Contingent valuation
Telemedicine Brick, 1997 Kaambwa, 2016 Basoglu, 2010 Dick, 2004 Tsuji, 2003
Lowitt, 1998 Spinks, 2016 Park, 2011 Marchell, 2017 Bradford, 2004
Mofid, 2007 Chang, 2017 Ahn, 2014 Andino, 2017 Bradford, 2005
Vandelanotte, 2011 Cranen, 2017 Morland, 2019 Qureshi, 2007
Jung, 2012 Snoswell, 2018 Stephen, 2014
Stypulkowski, 2015 Deidda, 2018
Lal, 2015 Chudner, 2019
Wallin, 2016
Nagao, 2019
Slightam, 2020
Barsom, 2020
Patient portal Hassol, 2004 Determann, 2016 Grande, 2013 Cabitza, 2015
Johnson, 2012 Patil, 2016 White, 2016
Quinlivan, 2014 Wildenbos, 2018
Saraswathula, 2018 Woolen, 2019
Cronin, 2018
mHealth Basu, 2011 Nayyar, 2018 Russell, 2018
Muench, 2014 Lim, 2020
Granger, 2016
Ray, 2016
Plinsinga, 2019
Nguyen, 2020
Multiple HITs Ranney, 2012 Boyde, 2018 Offermann-van Heek, 2019 Choudhry, 2015
Cabarrus, 2015 Brazeal, 2017
Apolinario-Hagen, 2018
Edwards, 2020

In comparing across healthcare contexts, we found that studies fell into one of three groups including measuring preferences for: (I) receiving health data, (II) receiving healthcare, and (III) managing health data and healthcare. Ten studies were categorized as regarding the receipt of health data (Table 1). This included studies assessing receipt of clinical information such as biopsy results or radiological reports. Thirty-three studies described preferences for HIT with regards to receiving healthcare (Table 1). These explored preferences for clinical interventions, therapies, or rehabilitation. Clinical areas of study in this category included diabetes management (58), mental health/psychological interventions (76,77), cardiology services (51), skin cancer screening with teledermatology (49,50,71,72,95), pain rehabilitation (69), and cardiac rehabilitation (56). Sixteen studies examined preferences for managing health data and healthcare (Table 1). These studies described preferences for EHR management and self-management of healthcare. Most studies in this area were process-oriented rather than clinically focused, although two studies did focus specifically on managing healthcare with regards to osteoarthritis (89), and adverse event reporting following immunization (90).

Themes from key preference findings

Thematic analysis of the key preference results from all 59 studies (Table S2) resulted in the identification of 6 substantive themes regarding patient and end-user preferences for HIT. First, that preference for HIT vary based on patient characteristics. Second, communication modality preferences depend on the type of exchange. Third, HIT is preferred when it facilitates expedience and convenience. Fourth, patients are concerned with their data being used outside of direct clinical encounters. Fifth, patients care about the cost of HIT. Sixth, HIT should not sacrifice quality of care.

Preferences for HIT vary based on patient characteristics

Sub-group and heterogeneity analysis conducted in many studies revealed that demographic characteristics such as age (47,54,62,65,66,70,71,74,78,86,87,97,101), race (60,102), gender (51,62,86,97), education (62,86,87,101), income (46,70) and proximity to care (46,79) were associated with patient preferences for HIT. Younger patients and higher income patients generally placed higher utility on HIT services.

Communication modality preferences depend on the type of exchange

Patients in many studies preferred that new, sensitive, complex, or urgent health concerns be communicated through conversation with a provider rather than electronically (47,69,70,72,77,80,82,85,101,102). In one study, patients’ preferred direct physician communication particularly for the return of abnormal biopsy results (85) though other studies reported participant indifference to communication method (81,101) and one study reported preference for return of biopsy results via the telephone rather than in-person (102). As waiting time for in-person care increased, patients became more willing to accept electronic communication regarding health information (54,55,70,85). Patients also expressed preferences for different modalities and functionalities of HIT based on the information being communicated (e.g., preference for text message vs. email) (87,98-100).

HIT is preferred when it facilitates expedience and convenience

Several studies demonstrated that using HIT was preferred relative to a traditional, in-person appointment when it was less burdensome to patients. Participants in one study indicated that the ideal circumstance for HIT was one wherein the patient lived far away from the clinic and would save money by using HIT (48). Visits that were outside of work hours (57) or located near other normal activities were preferred (49).

Patients are concerned with data use outside of clinical encounters

Patients expressed preferences for sharing their healthcare data with their health care team to inform medical decision making and improve care quality (44,63). However, patients also expressed concerns regarding the use of their data to inform non-clinical encounters. Patients generally opposed the use of their health data for marketing purposes (60) and for pharmaceutical and academic research (44). In one study younger patients had more liberal preferences for the storage and use of sensitive information than older patients (44). Data storage was among the most important concern of people reluctant to use electronic medical record systems (53).

Patients care about the cost of HIT

Patients expressed concern for the cost of HIT services in several studies. Cost was the most important service attribute regarding telemedicine (58) and the most decisive attribute for those who refused the use of electronic health records (53). Respondents in several studies expressed that telemedicine should be of lower cost than in-person care (48,51). Recurring service fees for HIT was more important to patients than single-time costs associated with devices needed to facilitate visits (59). Numerous studies investigated WTP for diverse HIT services (46,49,50,58,59,64-68,96). Several of these studies found that increasing age was associated with decreased WTP for HIT (65,66).

HIT should not sacrifice quality of care

There was concern among patients that HIT offered lower quality care as compared to in-person visits (49). Among a group of patients who expressed preferences for in-person care over telehealth, care quality was rated as the most important attribute of healthcare (52). Comprehensiveness of care was a highly prioritized attribute (58) and patients were more likely to prefer video-based care if they believed all of their concerns could be addressed during the appointment (45). In two separate studies patients were indifferent to in-person vs. HIT-facilitated healthcare as long as the provider was a specialist rather a general practitioner (50,61).


The uptake of HIT over the past twenty years has altered the process of both providing and receiving medical care (103). The current review demonstrates that patients have distinct preferences regarding both their own use and their care team’s use of HIT, namely that HIT is more appropriate in some settings than others, and that it ought to be convenient, low-cost, and high quality. This review contributes to calls to use and evaluate technologies from the perspective of patients by characterizing preferences for both the context and modality of HIT.

Despite technological advances there continues to be low satisfaction and uptake of HIT. Holistic understanding of patient and other end-user preferences for HIT can inform patient-centered development and application of HIT which should improve uptake (104). Doing so has the potential to improve patient engagement in health and self-management of health conditions (105,106). In addition to improving uptake of HIT more broadly, preference research can also help identify how preferences might vary across sub-groups of patient populations. The current review identified that preferences for HIT vary based on characteristics such as age, income, and education. Optimizing use of HIT might require tailoring it to meet the needs of unique individuals or groups of patients.

Thematic analysis of primary preference outcomes from included studies revealed that patients are reluctant for their personal health data to be used for drug development research. This is an important finding in light of the 21st Century Cures Act which encourages the use of real-world data such as that from patient medical records (7). To be patient-centered means to act in ways that are consistent with patients’ preferences and values (19). There is a tension in how to be patient-centered in this context; patients may not want to share data but also may enjoy the benefit of more expedient access to treatments facilitated by the sharing of their health data. This concern warrants further consideration from informatic, regulatory and bioethical perspectives and research. Preference research itself may be a useful tool to parse out acceptable tradeoffs between data sharing, data privacy, and development of new drugs.

In addition to being an area that can be informed by patient preferences, HIT might also facilitate the collection of patient preference information. There is a growing call to systematically and routinely collect patient preference information (23). Almost all stated-preference research is conducted cross-sectionally and as a result is it unknown whether preferences change over time or in response to medical events. Integrating preference-elicitation tasks into medical data, much in the way that patient-reported outcomes are currently captured now, is a potential area for new research. Knowing patient preferences at the point-of-care—for many aspects of that care, not just for HIT specifically—could improve medical decision making in clinical settings.

It is important to note that not all preference studies need be complex and experimentally designed in order to provide meaningful information about what patients want, be it with regards to HIT or elsewhere. While there has been a surge in the use of experimental methods such as DCEs in many aspects of health (107), other approaches can also be fit-for-purpose and appropriate to gauge preferences. For instance, direct elicitation approaches, wherein the researcher directly asks the respondent about what they do or do not want, composed half of studies in the current review.

This review has several limitations. First, we opted to include only quantitative preference elicitation methods that required participants to make trade-offs. Other preference elicitation methods including qualitative approaches can also speak to patient and end-user preferences for HIT (26). Another limitation is that of HIT’s conceptual ambiguity and identifying what was and was not HIT. While we conducted a systematic search, it is possible that not all articles on the topic of HIT preferences were captured. One reason for this is the conceptual ambiguity surrounding HIT. Our search followed a very broad definition of HIT (2), as specific descriptions of what does and does not constitute HIT are somewhat lacking. Such conceptual ambiguity creates difficulty in defining appropriate search terms. While our search strategy was based on our selected definition, choosing a different definition of HIT may have modified the returned set of studies and altered findings. The current review primarily captured preferences for HIT related to telemedicine, EMR, patient portals, and mHealth. Today’s rapidly-evolving technological and informatics environment means that there continue to be changes in electronic delivery of health information. On the horizon we anticipate that more HIT literature will address wearables, wireless medical devices, and personalized care (108), as well as HIT in a peri-COVID-19 world.

The current review used thematic analysis to synthesize findings from the primary preference results. Standard considerations of both meta-synthesis and qualitative analysis apply here, including that there are methodological challenges in combing results across multiple studies and that the reviewers are instruments of the research processes (109,110).

The growth of HIT in wake of the COVID-19 pandemic strengthens the imperative of this work. COVID has acted as a push-strategy forcing the rapid rollout of HIT, rather than fostering a strategic rollout purposefully aligned with patient preferences. As HIT systems become ubiquitous in everyday medical care it is important that they be built with patient preferences in mind. As the digital era evolves it is important to consider not only whether people have access to and are able to adequately operate electronic health care services but also whether electronically-acquired information can be translated into positive health outcomes (107,111,112).

To further consider the preferences of patients in the construction of HIT systems, health information developers and health care administrators should seek to collect input from patient/caregiver end-users to identify and implement user-friendly systems that are responsive to patient need. However, healthcare administrators, rather than patients, are often primary stakeholders when examining organizational factors of patient-centeredness (113). As patients may have different ideal uses for HIT than other groups it would make sense to include patients in the identification of organizational outcomes for intervention. Failing to consider the wants of patients in the development of HIT systems may exacerbate health care disparities (114).


We would like to thank Tanya Huwig for her support in hand-searching journals as well as Naleef Fareed and Rebecca Carter for their input in conceptualizing the manuscript. An earlier version of this manuscript was completed as a Masters of Public Health capstone paper by Brent Weiss at The Ohio State University.

Funding: None.


Provenance and Peer Review: This article was commissioned by the Guest Editors (Naleef Fareed, Ann Scheck McAlearney, and Susan D Moffatt-Bruce) for the series “Innovations and Practices that Influence Patient-Centered Health Care Delivery” published in Journal of Hospital Management and Health Policy. The article has undergone external peer review.

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at

Peer Review File: Available at

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at The series “Innovations and Practices that Influence Patient-Centered Health Care Delivery” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See:


  1. Buntin MB, Burke MF, Hoaglin MC, et al. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood) 2011;30:464-71. [Crossref] [PubMed]
  2. Brailer D, Thompson T. Health IT strategic framework. Washington, DC: Department of Health and Human Services 2004.
  3. Act Enforcement Interim Final RuleHITECH. 2017. Available online:
  4. Health IT and Health Information Exchange. The Office of the National Coordinator for Health Information Technology. 2020. Available online:
  5. Alotaibi YK, Federico F. The impact of health information technology on patient safety. Saudi Med J 2017;38:1173. [Crossref] [PubMed]
  6. Health Information Technology Integration. Agency for Healthcare Research and Quality. Available online:
  7. 21st Century Cures Act, 2016. Available online:
  8. Kruse CS, Beane A. Health Information Technology Continues to Show Positive Effect on Medical Outcomes: Systematic Review. J Med Internet Res 2018;20:e41 [Crossref] [PubMed]
  9. Ammenwerth E, Schnell-Inderst P, Machan C, et al. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc 2008;15:585-600. [Crossref] [PubMed]
  10. Warraich HJ, Califf RM, Krumholz HM. The digital transformation of medicine can revitalize the patient-clinician relationship. NPJ Digit Med 2018;1:49. [Crossref] [PubMed]
  11. Hillestad R, Bigelow J, Bower A, et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005;24:1103-17. [Crossref] [PubMed]
  12. Hilty DM, Ferrer DC, Parish MB, et al. The effectiveness of telemental health: a 2013 review. Telemed J E Health 2013;19:444-54. [Crossref] [PubMed]
  13. Sittig DF, Singh H. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Cognitive informatics for biomedicine. Springer, 2015:59-80.
  14. Kellermann AL, Jones SS. What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Aff (Millwood) 2013;32:63-8. [Crossref] [PubMed]
  15. Sockolow PS, Liao C, Chittams JL, et al., editors. Evaluating the impact of electronic health records on nurse clinical process at two community health sites. NI 2012: 11th International Congress on Nursing Informatics, June 23-27, 2012. American Medical Informatics Association: Montreal, Canada, 2012.
  16. Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am Med Inform Assoc 2017;24:246-50. [Crossref] [PubMed]
  17. Cowan KE, McKean AJ, Gentry MT, et al. (eds). Barriers to use of telepsychiatry: clinicians as gatekeepers. Elsevier: Mayo Clinic Proceedings, 2019.
  18. Ludwick DA, Doucette J. Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. Int J Med Inform 2009;78:22-31. [Crossref] [PubMed]
  19. Institute of Medicine Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Academies Press (US), 2001.
  20. Hersh W. A stimulus to define informatics and health information technology. BMC Med Inform Decis Mak 2009;9:24. [Crossref] [PubMed]
  21. Asagbra OE, Burke D, Liang H. The association between patient engagement HIT functionalities and quality of care: Does more mean better? Int J Med Inform 2019;130:103893 [Crossref] [PubMed]
  22. Snyder CF, Wu AW, Miller RS, et al. The role of informatics in promoting patient-centered care. Cancer J 2011;17:211-8. [Crossref] [PubMed]
  23. Funding Priorities. Agency for Healthcare Research and Quality. 2020. Available online:
  24. Ryan M. Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Soc Sci Med 1999;48:535-46. [Crossref] [PubMed]
  25. Ryan M. Discrete choice experiments in health care. BMJ Open 2017;7:e015689 [PubMed]
  26. Medical Device Innovation Consortium (MDIC). A framework for incorporating information on patient preferences regarding benefit and risk into regulatory assessments of new medical technology. Available online:
  27. Crossnohere NL, Fischer R, Crossley E, et al. The evolution of patient-focused drug development and Duchenne muscular dystrophy. Expert Rev Pharmacoecon Outcomes Res 2020;20:57-68. [Crossref] [PubMed]
  28. Jackson Y, Janssen E, Fischer R, et al. The evolving role of patient preference studies in health-care decision-making, from clinical drug development to clinical care management. Expert Rev Pharmacoecon Outcomes Res 2019;19:383-96. [Crossref] [PubMed]
  29. Gangwar H. Review on IT adoption: insights from recent technologies. J Enterprise Information Manag 2014;27:488-502. [Crossref]
  30. Gagnon MP, Desmartis M, Labrecque M, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012;36:241-77. [Crossref] [PubMed]
  31. Kruse CS, Krowski N, Rodriguez B, et al. Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ Open 2017;7:e016242 [Crossref] [PubMed]
  32. Cox A, Lucas G, Marcu A, et al. Cancer Survivors' Experience With Telehealth: A Systematic Review and Thematic Synthesis. J Med Internet Res 2017;19:e11 [Crossref] [PubMed]
  33. Powell KR. Patient-Perceived Facilitators of and Barriers to Electronic Portal Use: A Systematic Review. Comput Inform Nurs 2017;35:565-73. [Crossref] [PubMed]
  34. McGinn CA, Grenier S, Duplantie J, et al. Comparison of user groups' perspectives of barriers and facilitators to implementing electronic health records: a systematic review. BMC Med 2011;9:46. [Crossref] [PubMed]
  35. Joy SM, Little E, Maruthur NM, et al. Patient preferences for the treatment of type 2 diabetes: a scoping review. Pharmacoeconomics 2013;31:877-92. [Crossref] [PubMed]
  36. Purnell TS, Joy S, Little E, et al. Patient preferences for noninsulin diabetes medications: a systematic review. Diabetes Care 2014;37:2055-62. [Crossref] [PubMed]
  37. Patient preference information-voluntary submission, review in premarket approval applications, humanitarian device exemption applications, and de novo requests, and inclusion in decision summaries and device labeling: guidance for industry, food and drug administration staff, and other stakeholders. Food and Drug Administration Staff, and Other Stakeholders, 2016.
  38. Janssen EM, Dy SM, Meara AS, et al. Analysis of Patient Preferences in Lung Cancer - Estimating Acceptable Tradeoffs Between Treatment Benefit and Side Effects. Patient Prefer Adherence 2020;14:927-37. [Crossref] [PubMed]
  39. Nowell LS, Norris JM, White DE, et al. Thematic analysis: Striving to meet the trustworthiness criteria. Int J Qual Methods 2017;16:1609406917733847 [Crossref]
  40. Braun V, Clarke V. Using thematic analysis in psychology. Quant Imaging Med Surg 2006;3:77-101.
  41. Leigh S, Ashall-Payne L, Andrews T. Barriers and facilitators to the adoption of mobile health among health care professionals from the united kingdom: Discrete choice experiment. JMIR mHealth and uHealth 2020;8:e17704 [Crossref] [PubMed]
  42. Kennedy C, Johnston K, Taylor P, et al. Determining clinician satisfaction with telemedicine. London, England: SAGE Publications Sage UK, 2003.
  43. Tasneem S, Kim A, Bagheri A, et al. Telemedicine Video Visits for patients receiving palliative care: A qualitative study. Am J Hosp Palliat Care 2019;36:789-94. [Crossref] [PubMed]
  44. Patil S, Lu H, Saunders CL, et al. Public preferences for electronic health data storage, access, and sharing - evidence from a pan-European survey. J Am Med Inform Assoc 2016;23:1096-106. [Crossref] [PubMed]
  45. Slightam C, Gregory AJ, Hu J, et al. Patient Perceptions of Video Visits Using Veterans Affairs Telehealth Tablets: Survey Study. J Med Internet Res 2020;22:e15682 [Crossref] [PubMed]
  46. Chang J, Savage SJ, Waldman DM. Estimating Willingness to Pay for Online Health Services with Discrete-Choice Experiments. Appl Health Econ Health Policy 2017;15:491-500. [Crossref] [PubMed]
  47. Ranney ML, Choo EK, Wang Y, et al. Emergency department patients' preferences for technology-based behavioral interventions. Ann Emerg Med 2012;60:218-27.e48. [Crossref] [PubMed]
  48. Kaambwa B, Ratcliffe J, Shulver W, et al. Investigating the preferences of older people for telehealth as a new model of health care service delivery: A discrete choice experiment. J Telemed Telecare 2017;23:301-13. [Crossref] [PubMed]
  49. Spinks J, Janda M, Soyer HP, et al. Consumer preferences for teledermoscopy screening to detect melanoma early. J Telemed Telecare 2016;22:39-46. [Crossref] [PubMed]
  50. Snoswell CL, Whitty JA, Caffery LJ, et al. Direct-to-consumer mobile teledermoscopy for skin cancer screening: Preliminary results demonstrating willingness-to-pay in Australia. J Telemed Telecare 2018;24:683-9. [Crossref] [PubMed]
  51. Deidda M, Meleddu M, Pulina M. Potential users’ preferences towards cardiac telemedicine: A discrete choice experiment investigation in Sardinia. Health Policy Technol 2018;7:125-30. [Crossref]
  52. Chudner I, Drach-Zahavy A, Karkabi K. Choosing Video Instead of In-Clinic Consultations in Primary Care in Israel: Discrete Choice Experiment Among Key Stakeholders-Patients, Primary Care Physicians, and Policy Makers. Value Health 2019;22:1187-96. [Crossref] [PubMed]
  53. Determann D, Lambooij MS, Gyrd-Hansen D, et al. Personal health records in the Netherlands: potential user preferences quantified by a discrete choice experiment. J Am Med Inform Assoc 2017;24:529-36. [Crossref] [PubMed]
  54. Wildenbos GA, Horenberg F, Jaspers M, et al. How do patients value and prioritize patient portal functionalities and usage factors? A conjoint analysis study with chronically ill patients. BMC Med Inform Decis Mak 2018;18:108. [Crossref] [PubMed]
  55. Woolen SA, Kazerooni EA, Steenburg SD, et al. Optimizing Electronic Release of Imaging Results through an Online Patient Portal. Radiology 2019;290:136-43. [Crossref] [PubMed]
  56. Boyde M, Rankin J, Whitty JA, et al. Patient preferences for the delivery of cardiac rehabilitation. Patient Educ Couns 2018;101:2162-9. [Crossref] [PubMed]
  57. Basoglu N, Daim TU, Topacan U. Determining patient preferences for remote monitoring. J Med Syst 2012;36:1389-401. [Crossref] [PubMed]
  58. Park H, Chon Y, Lee J, et al. Service design attributes affecting diabetic patient preferences of telemedicine in South Korea. Telemed J E Health 2011;17:442-51. [Crossref] [PubMed]
  59. Ahn J, Shin J, Lee J, et al. Consumer preferences for telemedicine devices and services in South Korea. Telemed J E Health 2014;20:168-74. [Crossref] [PubMed]
  60. Grande D, Mitra N, Shah A, et al. Public preferences about secondary uses of electronic health information. JAMA Intern Med 2013;173:1798-806. [Crossref] [PubMed]
  61. Nayyar A, Jadi J, Garimella R, et al. Are You on the Right Platform? A Conjoint Analysis of Social Media Preferences in Aesthetic Surgery Patients. Aesthet Surg J 2019;39:1019-32. [Crossref] [PubMed]
  62. Lim D, Norman R, Robinson S. Consumer preference to utilise a mobile health app: A stated preference experiment. PLoS One 2020;15:e0229546 [Crossref] [PubMed]
  63. Offermann-van Heek J, Ziefle M. Nothing Else Matters! Trade-Offs Between Perceived Benefits and Barriers of AAL Technology Usage. Front Public Health 2019;7:134. [Crossref] [PubMed]
  64. Tsuji M, Suzuki W, Taoka F. An empirical analysis of a telehealth system in terms of cost-sharing. J Telemed Telecare 2003;9:S41-3. [Crossref] [PubMed]
  65. Bradford WD, Kleit A, Krousel-Wood MA, et al. Comparing willingness to pay for telemedicine across a chronic heart failure and hypertension population. Telemed J E Health 2005;11:430-8. [Crossref] [PubMed]
  66. Bradford WD, Kleit AN, Krousel-Wood MA, et al. Willingness to pay for telemedicine assessed by the double-bounded dichotomous choice method. J Telemed Telecare 2004;10:325-30. [Crossref] [PubMed]
  67. Qureshi A, Brandling-Bennett H, Wittenberg E, et al. Willingness-to-Pay Stated Preferences for Telemedicine Versus In-Person Visits in Patients with a History of Psoriasis or Melanoma. Telemed J E Health 2006;12:639-43. [Crossref] [PubMed]
  68. Stephen C, Sultan H, Frew E. Valuing telecare using willingness to pay from the perspective of carers for people with dementia: a pilot study from the West Midlands. J Telemed Telecare 2014;20:141-6. [Crossref] [PubMed]
  69. Cranen K, Groothuis-Oudshoorn CG, Vollenbroek-Hutten MM, et al. Toward Patient-Centered Telerehabilitation Design: Understanding Chronic Pain Patients' Preferences for Web-Based Exercise Telerehabilitation Using a Discrete Choice Experiment. J Med Internet Res 2017;19:e26 [Crossref] [PubMed]
  70. Brick JE, Bashshur RL, Brick JF, et al. Public knowledge, perception, and expressed choice of telemedicine in rural West Virginia. Telemed J 1997;3:159-71. [Crossref] [PubMed]
  71. Lowitt MH, Kessler II, Kauffman CL, et al. Teledermatology and in-person examinations: a comparison of patient and physician perceptions and diagnostic agreement. Arch Dermatol 1998;134:471-6. [Crossref] [PubMed]
  72. Mofid M, Nesbitt T, Knuttel R. The other side of teledermatology: patient preferences. J Telemed Telecare 2007;13:246-50. [Crossref] [PubMed]
  73. Vandelanotte C, Duncan MJ, Plotnikoff RC, et al. Do participants' preferences for mode of delivery (text, video, or both) influence the effectiveness of a Web-based physical activity intervention? J Med Internet Res 2012;14:e37 [Crossref] [PubMed]
  74. Jung SG, Kweon HJ, Kim ET, et al. Preference and awareness of telemedicine in primary care patients. Korean J Fam Med 2012;33:25-33. [Crossref] [PubMed]
  75. Stypulkowski K, Uppaluri S, Waisbren S. Telemedicine for postoperative visits at the Minneapolis VA Medical Center. Results of a needs assessment study. Minn Med 2015;98:34-6. [PubMed]
  76. Lal S, Dell'Elce J, Tucci N, et al. Preferences of Young Adults With First-Episode Psychosis for Receiving Specialized Mental Health Services Using Technology: A Survey Study. JMIR Ment Health 2015;2:e18 [Crossref] [PubMed]
  77. Wallin EE, Mattsson S, Olsson EM. The Preference for Internet-Based Psychological Interventions by Individuals Without Past or Current Use of Mental Health Treatment Delivered Online: A Survey Study With Mixed-Methods Analysis. JMIR Ment Health 2016;3:e25 [Crossref] [PubMed]
  78. Nagao K, Bullard AS, Pasko LE, et al. Tablet-Based Hearing Test Among Child Clinical Populations: Performance and Preference. Telemed J E Health 2019;25:973-8. [Crossref] [PubMed]
  79. Barsom EZ, Jansen M, Tanis PJ, et al. Video consultation during follow up care: effect on quality of care and patient- and provider attitude in patients with colorectal cancer. Surg Endosc 2021;35:1278-87. [Crossref] [PubMed]
  80. Hassol A, Walker JM, Kidder D, et al. Patient experiences and attitudes about access to a patient electronic health care record and linked web messaging. J Am Med Inform Assoc 2004;11:505-13. [Crossref] [PubMed]
  81. Johnson AJ, Easterling D, Nelson R, et al. Access to radiologic reports via a patient portal: clinical simulations to investigate patient preferences. J Am Coll Radiol 2012;9:256-63. [Crossref] [PubMed]
  82. Quinlivan JA, Lyons S, Petersen RW. Attitudes of pregnant women towards personally controlled electronic, hospital-held, and patient-held medical record systems: a survey study. Telemed J E Health 2014;20:810-5. [Crossref] [PubMed]
  83. Saraswathula A, Lee JY, Megwalu UC. Patient preferences regarding the communication of biopsy results in the general otolaryngology clinic. Am J Otolaryngol 2019;40:83-8. [Crossref] [PubMed]
  84. Cronin RM, Conway D, Condon D, et al. Patient and healthcare provider views on a patient-reported outcomes portal. J Am Med Inform Assoc 2018;25:1470-80. [Crossref] [PubMed]
  85. Basu PA, Ruiz-Wibbelsmann JA, Spielman SB, et al. Creating a patient-centered imaging service: determining what patients want. AJR Am J Roentgenol 2011;196:605-10. [Crossref] [PubMed]
  86. Muench F, van Stolk-Cooke K, Morgenstern J, et al. Understanding messaging preferences to inform development of mobile goal-directed behavioral interventions. J Med Internet Res 2014;16:e14 [Crossref] [PubMed]
  87. Granger D, Vandelanotte C, Duncan MJ, et al. Is preference for mHealth intervention delivery platform associated with delivery platform familiarity? BMC Public Health 2016;16:619. [Crossref] [PubMed]
  88. Ray M, Dayan PS, Pahalyants V, et al. Mobile Health Technology to Communicate Discharge and Follow-Up Information to Adolescents From the Emergency Department. Pediatr Emerg Care 2016;32:900-5. [Crossref] [PubMed]
  89. Plinsinga ML, Besomi M, Maclachlan L, et al. Exploring the Characteristics and Preferences for Online Support Groups: Mixed Method Study. J Med Internet Res 2019;21:e15987 [Crossref] [PubMed]
  90. Nguyen MTH, Ott JJ, Caputo M, et al. User preferences for a mobile application to report adverse events following vaccination. Pharmazie 2020;75:27-31. [PubMed]
  91. Cabarrus M, Naeger DM, Rybkin A, et al. Patients Prefer Results From the Ordering Provider and Access to Their Radiology Reports. J Am Coll Radiol 2015;12:556-62. [Crossref] [PubMed]
  92. Edwards EA, Cote A, Phelps AS, et al. Parents of Pediatric Radiology Patients Prefer Timely Reporting and Discussing Results with Referring Providers. Acad Radiol 2020;27:739-43. [Crossref] [PubMed]
  93. Apolinario-Hagen J, Harrer M, Kahlke F, et al. Public Attitudes Toward Guided Internet-Based Therapies: Web-Based Survey Study. JMIR Ment Health 2018;5:e10735 [Crossref] [PubMed]
  94. Dick PT, Bennie J, Barden W, et al. Preference for pediatric telehome care support following hospitalization: a report on preference and satisfaction. Telemed J E Health 2004;10:S-45-53. [Crossref] [PubMed]
  95. Marchell R, Locatis C, Burgess G, et al. Patient and Provider Satisfaction with Teledermatology. Telemed J E Health 2017;23:684-90. [Crossref] [PubMed]
  96. Andino JJ, Guduguntla V, Weizer A, et al. Examining the Value of Video Visits to Patients in an Outpatient Urology Clinic. Urology 2017;110:31-5. [Crossref] [PubMed]
  97. Morland LA, Wells SY, Glassman LH, et al. What Do Veterans Want? Understanding Veterans' Preferences for PTSD Treatment Delivery. Mil Med 2019;184:686-92. [Crossref] [PubMed]
  98. Cabitza F, Simone C, De Michelis G. User-driven prioritization of features for a prospective InterPersonal Health Record: perceptions from the Italian context. Comput Biol Med 2015;59:202-10. [Crossref] [PubMed]
  99. White H, Gillgrass L, Wood A, et al. Requirements and access needs of patients with chronic disease to their hospital electronic health record: results of a cross-sectional questionnaire survey. BMJ Open 2016;6:e012257 [Crossref] [PubMed]
  100. Russell AM, Smith SG, Bailey SC, et al. Older Adult Preferences of Mobile Application Functionality Supporting Medication Self-Management. J Health Commun 2018;23:1064-71. [Crossref] [PubMed]
  101. Choudhry A, Hong J, Chong K, et al. Patients' preferences for biopsy result notification in an era of electronic messaging methods. JAMA Dermatol 2015;151:513-21. [Crossref] [PubMed]
  102. Brazeal HA, Holley SO, Appleton CM, et al. Patient preferences for breast biopsy result notification. Breast J 2018;24:448-50. [Crossref] [PubMed]
  103. Ommaya AK, Cipriano PF, Hoyt DB, et al. Care-centered clinical documentation in the digital environment: Solutions to alleviate burnout. Washington, DC: NAM Perspectives. National Academy of Medicine, 2018.
  104. van Gemert-Pijnen JE, Nijland N, van Limburg M, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res 2011;13:e111 [Crossref] [PubMed]
  105. Klein-Fedyshin MS. Consumer Health Informatics-integrating patients, providers, and professionals online. Med Ref Serv Q 2002;21:35-50. [Crossref] [PubMed]
  106. Samoocha D, Bruinvels DJ, Elbers NA, et al. Effectiveness of web-based interventions on patient empowerment: a systematic review and meta-analysis. J Med Internet Res 2010;12:e23 [Crossref] [PubMed]
  107. Clarke MA, Fruhling AL, Sitorius M, et al. Impact of Age on Patients' Communication and Technology Preferences in the Era of Meaningful Use: Mixed Methods Study. J Med Internet Res 2020;22:e13470 [Crossref] [PubMed]
  108. Digital health innovation action plan. FDA: Digital Health Program, 2017.
  109. Paterson BL, Thorne SE, Canam C, et al. Meta-study of qualitative health research: A practical guide to meta-analysis and meta-synthesis. New York: Sage Publications, Inc., 2001.
  110. Joffe H, Yardley L. Content and thematic analysis. Res Method Clin Health Psychol 2004;56:68.
  111. Van Deursen AJ, Helsper EJ. A nuanced understanding of Internet use and non-use among the elderly. Eur J Commun 2015;30:171-87. [Crossref]
  112. Van Deursen AJ, Van Dijk JA. The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media Soc 2019;21:354-75. [Crossref] [PubMed]
  113. Liberati EG, Gorli M, Moja L, et al. Exploring the practice of patient centered care: The role of ethnography and reflexivity. Soc Sci Med 2015;133:45-52. [Crossref] [PubMed]
  114. Mullangi S, Kaushal R, Ibrahim SA. Equity in the Age of Health Care Information Technology and Innovation: Addressing the Digital Divide. Med Care 2019;57:S106-7. [Crossref] [PubMed]
doi: 10.21037/jhmhp-20-105
Cite this article as: Crossnohere NL, Weiss B, Hyman S, Bridges JFP. Patient preferences for health information technologies: a systematic review. J Hosp Manag Health Policy 2021;5:25.

Download Citation