JOURNAL OF MEDICAL INTERNET RESEARCH

Mazzocato et al

Original Paper

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care:
Comparative Cross-Case Study
Pamela Mazzocato1,2, PhD; Jamie Linnea Luckhaus1,3, MPH; Moa Malmqvist Castillo1, MPhil; Johan Burnett4, RN;
Andreas Hager4, LLM; Gabriela Oates5, PhD; Carolina Wannheden1, PhD; Carl Savage1, PhD
1

Department of Learning, Informatics, Management and Ethics, Medical Management Centre, Karolinska Institutet, Stockholm, Sweden

2

Södertälje Hospital, Södertälje, Sweden

3

Participatory e-Health and Health Data, Department of Women’s and Child’s Health, Uppsala University, Uppsala, Sweden

4

Upstream Dream, Bromma, Sweden

5

Pulmonary, Allergy and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States

Corresponding Author:
Pamela Mazzocato, PhD
Department of Learning, Informatics, Management and Ethics
Medical Management Centre
Karolinska Institutet
Tomtebodavägen 18 A
Stockholm
SwedenPhone: 46 8 524 83 696
Email: pamela.mazzocato@ki.se

Abstract
Background: Patient-driven innovation in health care is an emerging phenomenon with benefits for patients with chronic
conditions, such as cystic fibrosis (CF). However, previous research has not examined what may facilitate or hinder the
implementation of such innovations from the provider perspective.
Objective: The aim of this study was to explain variations in the adoption of a patient-driven innovation among CF clinics.
Methods: A comparative multiple-case study was conducted on the adoption of a patient-controlled app to support
self-management and collaboration with health care professionals (HCPs). Data collection and analysis were guided by the
nonadoption, abandonment, spread, scale-up, and sustainability and complexity assessment tool (NASSS-CAT) framework. Data
included user activity levels of patients and qualitative interviews with staff at 9 clinics (n=8, 88.9%, in Sweden; n=1, 11.1%, in
the United States). We calculated the maximum and mean percentage of active users at each clinic and performed statisticalprocess control (SPC) analysis to explore how the user activity level changed over time. Qualitative data were subjected to content
analysis and complexity analysis and used to generate process maps. All data were then triangulated in a cross-case analysis.
Results: We found no evidence of nonadoption or clear abandonment of the app. Distinct patterns of innovation adoption werediscernable based on the maximum end-user activity for each clinic, which we labeled as low (16%-23%), middle (25%-47%),or high (58%-95%) adoption. SPC charts illustrated that the introduction of new app features and research-related activity had apositive influence on user activity levels. Variation in adoption was associated with providers’ perceptions of care processcomplexity. A higher perceived complexity of the value proposition, adopter system, and organization was associated with lower
adoption. In clinics that adopted the innovation early or those that relied on champions, user activity tended to plateau or decline,
suggesting a negative impact on sustainability.
Conclusions: For patient-driven innovations to be adopted and sustained in health care, understanding patient-provider
interdependency and providers’ perspectives on what generates value is essential.
(J Med Internet Res 2024;26:e50527) doi: 10.2196/50527
KEYWORDS
chronic illness; implementation; adoption; spread; patient-driven innovation; mHealth; mobile health; innovation; health careprovider; motivation; interdependency; adaptability
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Introduction
Patient-driven innovation in health care is an emerging
phenomenon. An example of coproduction and prosumerism(where consumers produce what they consume), patient-driven
innovations can be seen as the next logical step in health careevolution [1,2] and could fundamentally challenge the essenceof what it means to be a professional health care provider. The
number of publications about patient-driven or informal
caregiver-driven innovations (ie, innovations that are both
initiated and driven by patients or informal caregivers or both)
mostly concern chronic conditions and have increased
substantially in recent years [3]. However, the current literaturedoes not examine the factors that influence the adoption, spread,and scale-up of patient-driven innovations in health careorganizations [3]. The paucity of research studies evaluating
and reporting the outcomes of patient-driven innovations has
been suggested as a potential obstacle to their adoption in healthcare [4].
Cystic fibrosis (CF) is a complex chronic and genetic condition
that affects respiratory and other organ systems [5]. Diseaseactivity varies over time, and treatment requires a high degreeof discipline and self-care outside of the clinical microsystem[6,7]. Patient-driven innovations in CF care have resulted in thedevelopment and dissemination of mobile health (mHealth)apps that support patients with CF and their caregivers in
self-care and information sharing with health care providers[8-11]. Sharing of patient-generated health data has been
associated with improved symptom control and quality of lifeand reduced health care use [12]. The COVID-19 pandemic has
further demonstrated the value of and opportunity forpatient-generated health data [13], as well as the importance ofactively involving patients with CF and caregivers in criticalconversations about care and self-care management [14].
However, implementing innovations in health care can bechallenging [9,15-18]. The issue is even more pronounced if
innovations are created or driven by patients [19], as that canchallenge traditional hierarchical values and structures andprofessional identities. When implementation is not
appropriately managed, mHealth apps fail to be adopted, areabandoned, or falter when they are scaled up or spread [20,21].
To increase the ability of hospitals, staff, and patients to adopt
technological innovations, implementation approaches need to
be anchored in the needs of patients and adapted to theorganizational context and the wider system in which the newtechnology is implemented [21,22]. The level of organizationand system complexity will influence the level of adoption by
patients and providers [20,21,23-25].
Building on the field of complexity, we see complexity as acharacteristic and property that emerges from 3 variables: thenumber of elements or components, often referred to as nodes(eg, actors and stakeholders); the number of interactions andinterdependences between these nodes; and the variation withinthese nodes and interactions [26-28]. Successful implementation
often requires a higher level of alignment between the purposeof the organization and its users [20,21,24]. The risk for failure
increases with the level of complexity as health care systems
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respond to changes in unpredictable and nonlinear ways due tofuzzy organizational boundaries and interconnected actions withother actors that are often difficult to predict or even be awareof [21,29].
With patients and informal caregivers playing an increased rolein health care, we need to know more about how their potential
contributions can be realized through the adoption ofpatient-driven innovations in health care. Therefore, the aim of
this study was to explain variations in the adoption of apatient-driven innovation among CF clinics. We posed thefollowing research questions:••

How does the adoption of a patient-driven innovation, basedon patterns of patient use, vary among clinics?
What factors influence the level of adoption of thepatient-driven innovation over time?

Methods
Study Design
This comparative multiple-case study used mixed (quantitativeand qualitative) methods to explain differences in the adoptionof a patient-driven innovation among patients with CF at 9
clinics. The Consolidated Criteria for Reporting Qualitative
Research (COREQ) guideline [30] was followed in reporting
this study (Multimedia Appendix 1). The study is part of thePatient in the Driver’s Seat research program conducted at the
Medical Management Centre, Karolinska Institutet, Sweden. It
is a 6-year program that studies how 5 patient-driven innovations
are implemented in clinical practice and the daily lives ofpatients and their networks, 1 of which is the subject of thisstudy.

Theoretical Framework
This study was guided by the framework for theorizing andevaluating nonadoption, abandonment, and challenges in thescale-up (ie, building infrastructure to support full-scale
implementation across an organization, locality, or healthsystems), spread (ie, replicating an intervention somewhereelse), and sustainability of health and care technologies(nonadoption, abandonment, spread, scale-up, and sustainability[NASSS]) and the NASSS complexity assessment tool
(NASSS-CAT) [29]. We chose the NASSS framework because
it was designed to both analyze and prepare for the
implementation of technology in health care. Its development
process has been well described, lending surface validity to theframework, and it has garnered attention among researchers[20,31,32]. According to the framework, the dynamic
interactions that influence the nonadoption, abandonment,
scale-up, spread, and sustainability of technological innovations
are inherent to the complexity within and between 7 domains:••••

Condition: nature of the condition or illness, comorbidities,
sociocultural factors
Technology: material features, type of data generated,knowledge needed to use, supply model
Value proposition: supply-side value for the developer andfor the patient
Adopters: staff, patients, caregivers
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Organization: capacity to innovate, readiness fortechnology, nature of adoption/funding decision, extent ofchange in team routines, work needed
Wider
context:
political/policy,
regulatory/legal,
professional and sociocultural
Embedding and adaptation over time: scope, organizational
resilience [29]Simple systems consist of a few components that interact instraightforward and predicable ways. Complicated systems havemultiple components that interact in a predictable fashion.
Complex systems have multiple and intricately related
interactions that are constantly changing, unpredictable,
nonlinear, and difficult to deconstruct [29]. NASSS and,practically, CAT can be used to distinguish between simple,
complicated, and complex elements in the 7 domains [29]. The
intention is to identify the multiple influences that are at play;
to determine how complexity once identified can be reduced,
addressed, or navigated; and to provide information andguidance to the involved actors on how to do so.

The Patient-Driven Innovation
The technology (innovation) studied was a patient-controlled
app (named Genia) that was originally designed as apatient-facing app to foster self-management. With the additionof a function to generate reports for providers, the app expanded
its scope to become a patient-controlled information app for the
coproduction of care that places the patient at the center of thedecision-making process [10,11]. Founded in 2012 by a fatherof children with CF, Upstream Dream, which developed thepatient-driven innovation, employs individuals with lived
experience as patients or informal caregivers of a family member
with a rare disease. The innovation was developed incollaboration with the Swedish CF community and upon
research conducted at Karolinska Institutet and Dartmouth
University. Launched in Sweden in 2015, it was introduced to
all CF clinics in the country within a period of a few months.
Subsequently, the innovation was piloted (2020-2021) andadopted (2021) as part of routine care in 1 pediatric CF programin the United States and is now the focus of a multicenter study.
At launch, the patient-driven innovation was only compatible
with the iOS platform. Android support was added later.
Upstream Dream is working to spread the innovation to otherclinics in the United States and South America. The innovation
has also been tested for use with other chronic medical
conditions and has demonstrated improved patient engagement,
patient-centered care, and practice-based learning, with theconclusion that the innovation can be recommended for otherchronic conditions [31].
The main features of the patient-driven innovation are related
to the tracking of symptoms and medications. Whatdifferentiates it from a regular personal health record for patientsto record disease progression is that information can be sharedwith care providers, which is why the app was introduced
through the clinics to reach patients with CF. Information thatpatients wish to share with their multidisciplinary clinical careteam is submitted via previsit reports in the form of portable
document format (PDF) files. Data are integrated into the
Swedish National Cystic Fibrosis Quality Registry. In the United
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States, data are incorporated into the local electronic healthrecord (EHR) [9]. Over time, additional features were added
based on input from the clinics. These included an antibiotic
use–reporting tool, “Antibiotic Check-in,” to support care andresearch on the use of new antibiotics and therapies; amedication-monitoring tool for the Orkambi medical treatment;
and a “Health Check-in” feature to simplify communication of
the information used by clinics for planning meetings and patient
visits.
In Sweden, clinics were not charged to use the innovation,
whereas in the United States, clinics paid an annual fee. The
innovation was free for patients to use in both countries.
Patients’ user activity can be seen as an outcome that reflects
how well clinics integrate the app in their own care processes.
When the study was conducted, 2 generic health informationand communication apps were provided by the regions and inuse in the Swedish clinics, “1177” and “Always Open.” Neither
app provides a comparable service (patient-controlledcommunication of disease activity) to the innovation; instead,all 3 apps provide complementary functions that did not interferewith one another and could therefore be used simultaneously.
The 1177 app is a patient portal that provides information about
illnesses and clinics, booking, and electronic medical records.
The Always Open app (Swedish Alltid Öppet) was designed asa secure platform for providing remote care services, such asappointment reminders, prescription renewals, and video visits.
Its availability was limited to 1 region, so only 2 clinics hadaccess to it.

Study Setting
The study was conducted at 1 CF center in the United States(referred to as clinic A) and all 8 pediatric and adult CF centers
in Sweden (referred to as clinics B-I). All but 1 clinic had used
the innovation for 7 years (2 years for the most recent clinic).
The clinics were small, with 7-10 staff members (typically
including pulmonologists, registered nurses (RNs),
physiotherapists or respiratory therapists, psychologists or social
workers, and dietitians), and focused specifically on CF. All
were located at academic medical centers. One clinic in Sweden
provided both pediatric and adult care, and the same staff served
both patient populations. However, because adoption levels
were different between these 2 patient groups, the data were
presented separately.
Based on the inclusion of many different implementation
settings, we expected different patterns of adoption. This would
allow us to develop an empirical basis for both literal replication(cases that predict similar results) and theoretical replication(cases that predict different results for predictable reasons) [32].
The US site was selected because it was the first use case outside
Sweden. We expected its inclusion to provide further insights
into relevant contextual aspects.

Data Collection
Quantitative data were collected to assess the level of adoption
based on the number of active users (ie, patient activity). Since
the innovation was designed to improve patient-provider
communication, patient activity levels can be seen as anindicator of adoption. Anonymous user activity data for each
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clinic were extracted from Upstream Dream’s monthly reports
on user activity for the period from March 2015 (when theinnovation was implemented at the first clinic) to March 2022.
At clinic A, where the innovation was implemented last, theuser activity period was from February 2020 to March 2022.
Qualitative data were collected to understand how the innovation
was perceived and used based on the NASSS domains and to
identify specific interventions or factors that may haveinfluenced user activity. A semistructured interview guide wasdeveloped in both Swedish and English (Multimedia Appendix2). Questions were designed to elicit information about NASSS
domains and their degrees of complexity [21], as well as tocapture information about app integration into care processes.
A precursor to the interview guide was first tested in a separate
study [33] and revised based on that experience. Further input
was sought from Upstream Dream to ensure that we would
capture a holistic understanding of how the innovation was usedin daily practice with respect to the different organizational
contexts and potential differences in complexity. The interview
guide was then piloted twice, with minor changes made toenhance the clarity of the questions in only the Swedish version.
Purposive and snowball sampling strategies were used to
identify participants with knowledge of and personal experience
with the innovation and who represented the professions
involved in CF care. Upstream Dream’s clinical coordinators
contacted the staff coordinators at each CF center on our behalf,
who then connected us with the staff who expressed an interest
to participate in the study. We contacted these individuals via
email and followed up by telephone. In total, 21 participants
were interviewed, including 2 key members of Upstream Dream,
one of whom was interviewed twice (first, to develop acontextual understanding of the innovation and the supplierperspective and then to provide additional information andclarity after the adopter interviews), RNs, physicians, respiratory
therapists or physiotherapists, a psychologist, a dietician, anda researcher involved in the development, implementation, andevaluation of the innovation in 1 of the clinics, without beinga health care provider. This role distribution reflected typicalstaff distribution at the clinics. The CF centers involved had asmall number of employees, which limited the number ofpotential participants. The number of participants was also
limited by the high workloads brought on by the COVID-19
pandemic. Thus, the participants involved represented between
28% and 40% of working staff. Participants’ experiences with
the innovation ranged from 3 months to 7 years.
Interviews were conducted online via the Zoom
videoconferencing system due to the ongoing COVID-19
pandemic, in Swedish or English, and lasted 30-60 minutes.
Interviews were conducted by 2 authors with training inqualitative research, with support from a senior researcher. Theinterviewers had no prior relationship with the participants.
Audio was digitally recorded and transcribed verbatim.

Data Analysis
Quantitative data on user activity were analyzed using
descriptive statistics. We calculated the percentage of activeend users per month among the total number of patients and thepercentage of active users on average over the entire period.
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Active users were defined as end users who logged on and usedsome of the basic app features within a 6-month period. As
clinic A first introduced the patient-driven innovation as a pilot
study, the percentage of active users for that period was
calculated based on the pilot sample size and after the end of
the pilot was calculated based on the clinic size. The descriptive
statistics (ie, maximum end-user activity and average over theentire period) provided insights into the level of adoption. We
defined low (<25%), middle (25-50%), and high (>50%) activity
levels based on the maximum percentage of end-user level
achieved.
To put user activity data into an organizational context, we
complemented the descriptive data with statistical processcontrol (SPC) charts. SPC charts were created for each clinic
to identify whether and when statistically significant changes
in app adoption levels occurred and whether these were
sustained. SPC makes it possible to determine whether a change
is a matter of chance (ie, common-cause variation) or due to a
specific happening or intervention (ie, special-cause variation)[34-36]. We used P charts with the following rules to identify
special-cause variation: rule 1/3 sigma violation (1 point +/–the upper control limit/lower control limit [UCL/LCL], withthe control limits set to +/–3 sigma), rule 2/shift (8 successive
consecutive points above or below the centerline), rule 3/trend(6 or more consecutive points steadily increasing or decreasing),
and rule 5/hugging the centerline (15 or more consecutive points
within +/–1 sigma on either side of the centerline) [37].
Qualitative data were analyzed using content analysis (directed
and inductive) [38], process mapping, and complexity analysis.
Interview transcripts were read through repeatedly to develop
familiarity and then subjected to directed qualitative content
analysis [39]. Two authors together identified meaning units,
which they abstracted to condensed meaning units and added
as “sticky notes” to the MIRO online whiteboard for visualcollaboration, where they were directed to 1 of the NASSS
domains (ie, categories). The condensed meaning units werelabeled with descriptive codes. Where the codes did not fit theframework, additional subcategories were created throughtraditional inductive content analysis [39]. All codes and thecategorization were reviewed and corroborated by 2 otherauthors. During the analysis, interview data from suppliers and
HCPs were kept separate, and the former were used solely toprovide contextual information about specific interventions andfactors that were integrated in the SPC charts (eg, timeline ofinterventions that could have influenced user activity).
To support the cross-case comparison, the traditional approachto reporting qualitative analyses was then transformed into
tabulated form based on the original NASSS framework andexpanded to include the additional subcategories.
Process maps were created for each clinic based on interview
data to illustrate how the innovation was integrated into clinicalwork processes. The process maps and the qualitative analysis
were shared with participants in an informant validation process.
Five clinics made small adjustments. The suppliers provided
additional corroborating feedback, where needed, for the clinics
that did not respond.

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We diagnosed complexity levels (see the definitions of simple,
complicated, complex in the theoretical framework) of the
NASSS domains for each clinic by analyzing interview datausing the NASSS complexity table (Multimedia Appendix 3)
and the NASSS-CAT Short survey, which was specificallydesigned to assess and differentiate between complexity levels[29]. Both analyses were combined to generate the complexity
assessment. Although the survey was originally intended tospark a reflective discussion, we used the reflective discussion
created in the interview setting to diagnose the level ofcomplexity. For the qualitative analyses and the complexity
assessment, conflicting interpretations were discussed until
consensus was reached.
Qualitative data were first analyzed case by case but thentriangulated with quantitative data to develop initial explanationsof variation in adoption. These were then tested against the data
and the analyses to identify those domains or interacting
domains that could explain the observed patterns. In a processakin to modified analytic induction [38], when falsifying
evidence was found, the explanatory model was dropped. This
process involved vigorous discussion and iterative cycles tonarrow and refine the explanatory models that are presented in
the cross-case comparison.

Ethical Considerations
Ethical approval was obtained from the Swedish Ethical Review
Authority (approval number 2019-03849). The study followed
the Swedish Research Council’s ethical principles for humanities
and social science research. Participants were informed orally
and in writing about their rights and what study participation
would entail. Written and verbal consent was obtained from allparticipants prior to commencement of interviews. Quantitative
data were completely anonymous. Qualitative data were
pseudonymized and deidentified prior to coding and analysis.
In presenting the findings, we made efforts to maintainparticipants’ privacy and confidentiality, referencing HCPs onlywith their pseudonym identifiers and clinic letters (eg, “HCP01,
clinic A”). No participant received any compensation forparticipating in the study.

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Results
In this section, we present the triangulation of the qualitativeand quantitative data first with user activity levels, and then across-case comparison based on the complexity assessment.

Participant Details
In total, 21 participants (n=16, 76.2%, women and n=5, 23.8%,men) were interviewed. Of the 21 participants, 9 (42.9%) wereRNs, 5 (23.8%) physicians, 4 (19%) respiratory therapists orphysiotherapists, 1 (4.8%) psychologist, 1 (4.8%) dietician, and
1 (4.8%) researcher.
In 2 of the clinics (D and H), we were able to interview only 1(4.8%) participant each; the remaining 7 clinics were represented
by 2 (9.5%) or more participants: clinic A, n=3 (14.3%); clinic
B, n=4 (19%); clinic C, n=4 (19%); clinic E, n=2 (9.5%); clinic
F, n=2 (9.5%); clinic G, n=2 (9.5%); and clinic I, n=2 (9.5%).

User Activity Level
The end-user activity levels showed that the innovation was
adopted by patients at all clinics; there was no evidence of
nonadoption. Based on the maximum end-user level achieved,
we found 3 clusters: high-adoption clinics A (maximum n=40,65%), B (maximum n=60, 58%), and C (maximum n=66, 96%);
medium-adoption clinics D (maximum n=125, 35%), E(maximum n=30, 47%), and F (maximum n=60, 25%); andlow-adoption clinics G (maximum n=120, 19%), H (maximum
n=116, 23%), and I (maximum n=155, 16%).
To further explore user activity levels over time, SPC charts
were created (Figure 1) for each clinic to plot the user activity
level over 81 months. In the SPC charts, specific interventions
or events retrieved through the interviews, which may haveinfluenced user activity levels over time, are indicated as verticaldotted lines and labeled as a-g. These included the introductionof new features, research-related activity, and the COVID-19
pandemic.

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Figure 1. SPC analysis of end-user activity at the different clinics organized as high (top row), middle (middle row), and low (lower row) end-user
adoption. The UCL and LCL were defined as +/– 3 SDs from the centerline [37]. The y-axis uses different scales to be able to better discern the trends
over time. The blue circles represent common cause variation, whereas the red squares represent special cause variation, with numbers indicated according
to SPC rules. LCL: lower control limit; SPC: statistical process control; UCL: upper control limit.

New features included the launch of Orkambi medication
monitoring (intervention a) in month 42, which likely explains
the increased use among 5 clinics (B-F), particularly among
pediatric clinics. Antibiotic Check-in was launched in Swedishclinics in month 62 (intervention b), which was followed by acampaign (intervention d), which could explain the increased
activity in 7 (87.5%) of the 8 Swedish clinics (C-I). The
introduction of an Android-compatible version in month 68(intervention e) opened the innovation up to all patients andcaregivers with a smartphone or tablet. Health Check-in(intervention f) may have contributed to the observable increasearound month 70 (clinics C, D, F, and H).
The influence of research-related activities was mainly identified
in clinic A, in which there was a rapid increase in activity
starting month 60, which plateaued. This corresponded to when
the innovation was first introduced in clinic A as a year-longsingle-group pilot study (n=40 participants, pre-post design)[40]. When the patient quota for the pilot was met (first verticaldotted line, Figure 1), 40% of the invited patients were active
users. Activity declined after the pilot ended but began to riseagain (month 81) when the decision was made to launch theinnovation clinic-wide. The consistency of this increase
continued after the last point recorded in Figure 1 until activity
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included in the SPC). Clinic C launched a digitization research
project (intervention c), which required participants to use theinnovation, and later launched a second digitization project(intervention g) in month 79.
The COVID-19 pandemic also appeared to influence user
activity levels. Clinic A partially transitioned to telehealth visits
during the pandemic, which made the Health Check-in featuredesirable as patients could upload photos and other information.
This may have contributed to increased user activity. In contrast,
for clinics E, F, H, and I, a deterioration (months 70-78) wasconcomitant with an active choice not to focus on the innovation
due to strained resources.

Cross-Case Comparison: Complexity Assessment
Linked to Adoption Level
Specific characteristics of the clinics related to each NASSS
domain gleaned from the interviews and the process maps arepresented in Multimedia Appendix 4. In terms of complexity,
all clinics viewed the nature of the condition, technology, andwider context domains as complicated (Figure 2). Differenceswere found in the value proposition, adopter system,
organization, and embedding domains. The cross-casecomparison presented next was organized around levels ofadoption, integrating data from the SPC analysis and exploring
differences and similarities in the NASSS domains.

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Figure 2. Complexity assessment for NASSS domains grouped by adoption level based on maximum end-user activity. Green circles represent simple,
blue labyrinths complicated, and red network symbols complex. *Minimum activity levels were not reported because data collection started when there
were 0 active users for all clinics. **For clinic A, n was calculated based on active users over the pilot study. NASSS: nonadoption, abandonment,
spread, scale-up, and sustainability.

High-Adoption Cluster (Clinics A-C)
The first group perceived the value proposition as simple, theadopter system and organization as complicated, and embedding
as either simple or complicated. The staff had a sharedperception that there is a clear benefit to using the innovation(ie, the innovation-facilitated meeting of patient and provider
creates value in health care for this chronic condition). This wasaligned with their view that value in health care for this chronic
condition is created in the patient-provider meeting. Although
going through reports lengthened the previsit planning process,the innovation was perceived to save time through more concise
patient visits.
Just doing that, [opening reports in weekly teammeetings] will change it a little bit, it is going tolengthen our [team] meeting a bit (…) So it helps
everybody to be prepared ahead of time and hopefully
make the visit more concise. [HCP14, clinic A]
For clinics B and C, where Antibiotic Check-in was used, thesedata were highly valued for research purposes and research wasseen as an integral component of high quality care. For clinic
C, this was corroborated by SPC analysis (intervention c).
The adopter system was viewed by clinics A-C as complicated.
Patient-controlled data were viewed as integral to previsit
planning, suggesting a view of patients as active contributors
to the cocreation of care and of the staff as having a
responsibility to ensure that patients contributed with these data.
Way more attention is being paid now to thispatient-generated data rather than the core clinicaldata. I mean, we still look at lung function andmicrobiology and what not, but patient-reported
symptoms and outcomes are a bigger part of the
discussion now, as well as patient preferences
and…and goals and what people want to focus on.
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So that is 1 change that has happened. [HCP15, clinic
A]
This person-centered culture was reflected in the organization
domain, which the staff perceived as complicated, asorganizational routines and care pathways needed to be flexible
to reinforce the importance of patient input and the use of theinnovation as an integral source of information for previsit
planning. For example, if a patient had either not downloaded
the innovation or submitted a report, clinics A and C had
routines to complete those tasks together with the patient(Multimedia Appendix 4). Even though these clinics saw a clear
value in the innovation, they still expressed the difficulties ofcultural change and that it requires key drivers.
We often do not have the personnel for this. It needs
to be carried out, in addition to the usual work. And
there has to be someone driven to be able to pushthese questions forward. [HCP01, clinic C]
Embedding was seen as simple or complicated based on how
mature the routines for incorporating user information (thereports) into the patient pathway were. In clinic B, the continual
increase in adoption observed in Figure 1 could be explainedby the clinic’s routines of using the innovation with the wholeclinical team, as well as its regular communication with thedeveloper. For example, clinic A had clear plans and usedcollective reflection among the staff for embedding thetechnology in the short and the long term.
The good thing is that the way we have set up thispragmatic trial is that they are [clinicians] notrequired to do anything extra than they normally do,so they are not required to log in on a separate
platform or a dashboard or anything extra. Everythingis embedded and integrated, which makes it an easiersell. [HCP15, clinic A]

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Clinics A-C saw a match between what they valued (thepatient-provider interaction) and the value proposition of theinnovation because it improved the quality and efficiency ofthe patient-provider encounter. These clinics had existing andfurther developed their routines to ensure and reinforce patientuse of the innovation. Moreover, they behaved as if they“co-owned” the innovation either by conducting research studies
or by taking responsibility for patient training. In the adopter
system, most of the staff had clearly defined roles in relation to
the use of the innovation, as well as established routines forgroup reflection. Participants valued patients’ role in thecocreation of care.

Middle-Adoption Cluster (Clinics D-F)
The second group perceived the value proposition and adopter
system as complicated and organization and embedding ascomplex. The value proposition was seen as complicatedbecause the technology’s desirability was contested, and thebusiness case for adoption was deemed unclear. The participants
experienced a mismatch between the condition and technologythat reflected itself in their view of the value proposition: clinics
D and F (adult clinics) believed that the technology better serves
pediatric patients, who typically receive outpatient care andneed to report symptoms, whereas adult patients with CF in
Sweden are often hospitalized and their symptomatology easierto track. The staff felt that the graphical user interface is lessappropriate for adults.
Regarding the adopter system, clinics E and F questioned theappropriateness of care providers in a public institution toconvince their patients to use a product from a private company.
If a patient is completely on top of it with theirtreatment and medications and everything, then it
feels a little like a car salesman if I am to try and sellsomething that is not a directive of the hospital. And
that role we all feel is a bit annoying. But if you canshow a direct benefit to using [the innovation], thenit feels good. [HCP12, clinic F]
Organization was seen as complex because of difficulties
integrating the innovation in the workflow. For example,
patient-generated reports were scheduled on the weekly agenda
at clinics E and F but seldom discussed. Work routines also
included contact with the supplier, who felt that clinic D
demonstrated heightened commitment. This was mirrored in
the SPC data, which showed a clear increase in adoption toward
the end of the data collection period. However, patient workflow
processes were not established. Adoption for all 3 clinics waslargely the responsibility of individual clinicians and, due toindividual levels of enthusiasm or work practices, gave rise tovariation (Figure 1). This impacted patients.
We are a center where not everyone is on the teamin the same way. I think that is a factor for whether,
depending on which doctor one meets, there will bea question about [the innovation] or not (…) It
becomes person dependent in a crazy way. [HCP19,
clinic E]
It impacted staff as well.

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Mazzocato et al
If everyone did it, there would not be any extra work,
but since we cannot manage to get everyone here
doing it, I end up trying to take the main responsibility(…) Sometimes I sit and go through [the reports] (…)
So, I have a bit of extra work, but it is too difficult tocreate a routine for such a thing if not all patientsuse it, then it can become forgotten. [HCP18, clinic
H]
In clinics E and F, adoption relied heavily on champions, which
could explain the decrease observed in months 26-43 (Figure1), which was also compounded by a severe staff shortage.
Embedding was deemed complex at clinics E and F as they
were forced to prioritize resources due to the COVID-19
pandemic, which shifted focus away from the innovation andwas reflected in the user activity decrease. Moreover, they felt
the questions prior to the introduction of Antibiotic Check-in
and Health Check-in were too generic.

Low-Adoption Cluster (Clinics G-I)
The third group perceived the value proposition, adopter system,
organization, and embedding as complex. Like clinics D-E,
these providers had a negative experience of advocating for a
privately owned app. They were among the first to adopt theinnovation, at a point where app features were minimal and theapp was limited to 1 platform. They felt that this effort to getpatients to use an underdeveloped app negatively impacted thepatient-provider relationship. The SPC analysis for clinics H
and I (Figure 1) confirmed that early adopters struggled tomaintain adoption as the user activity level either remained
stable (clinic H) or decreased (clinic I). The addition of newfeatures, although potentially increasing the value of theinnovation, was offset by previous experiences, which had wornthem out.
It is now actually that you should start trying to getpatients to use [the innovation]. But in [this region],
we have sort of worn ourselves out because wealready did it 4-5 years ago. [HCP18, clinic H]
The innovation was seen as undesirable by most of the staff atthese clinics, and they perceived their patients were equally
uninterested. Several providers mentioned that patients do notwant to “have their illness in an app,” as symptom tracking canbecome a reminder of how sick one has been and add yet anothertask, when patients with CF are already “drowning in healthcare” (HCP06). Some also felt adults have a hard time changing
their ways. These opinions demonstrated a mismatch between
the value of the technology and the needs and challenges related
to the condition.
Adopter systems were deemed complex because, although at
all clinics the staff had to learn new skills, the staff at clinics
G-I felt the innovation poses a threat to their professional
identity and scope of practice and felt patients find theinnovation challenging. Clinicians preferred digital technologies
available from the regions, rather than from a private company.
Here, they referred to 1177 and Always Open as examples ofsuch tools, which they also perceived served a clearer purpose.
Clinicians were skeptical of how the app was introduced andthat it did not come from within the clinic.
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It was not we as providers who went to an app
developer and said, “We want a tool.” Rather, it wasthey who came from the outside and said, “You needa tool, and here it is.” [HCP03, clinic G]
Clinicians at pediatric clinics believed there is a particular needfor medication tracking among adult patients, whereas the staff
at adult clinics expressed the opposite. Past experiences withother technological interventions and how well they werereceived also influenced how hesitant or open clinics were to
innovation.

Mazzocato et al
al [8] found, “Self-management enfolds a collaboration between
patients and [health care providers].” This suggests that studying
providers’ experiences of using patient-driven innovations canbe an important perspective as patients’ and providers’ behaviors
are 2 sides of the proverbial same coin. One could expect thata patient-driven innovation, as an example of prosumerism,
would entail a higher level of acceptance or be more highly
valued than an innovation external to the clinical context.

Principal Findings

All clinics seemed to agree that there is a clear need to focus
clinic visits on what patients value. Where the value proposition
was perceived as simple, HCPs saw the innovation as a solution,
or at least worth testing as a solution. Although going throughreports lengthened weekly previsit planning, the staff saw thatthe innovation enables shorter and more concise patient visits[9]. This mirrors the findings for another patient-centered careapp for patients with CF [8]. Clinics with lower adoption
described the innovation as interfering with the patient-provider
interaction, since they believed they already knew their patientswell due to the chronic nature of CF and perceived the app asan affront to their professionalism. The time providers spent
with their patients to elicit this information was seen as ademonstration of how they valued their patients. The difference
between the 2 interpretations of value could be paraphrased,from the perspective of the professional, as “we value ourpatients’ time” versus “we value our time with patients.” In theformer, providers focus on what matters to the patient with thehelp of the innovation; in the latter, providers try to find out
what is the matter with the patient through a person-to-person
conversation without the innovation and the information it
provides.

In this study, the adoption of a patient-driven innovation wasstudied using a complexity-based framework and tools for the
introduction of technology in health care, NASSS-CAT. The
innovation was developed as a patient-controlled information
app to support the self-management of CF and communicate
disease related–activity with health care providers. Although
we found no evidence of nonadoption or clear abandonment of
the app, distinct patterns of innovation adoption were discernable
based on user activity data (ie, low, medium, and high adoption).
The perceived value proposition of the technology and theexperienced complexity were associated with different levelsof adoption. Research activity and the introduction of new app
features positively impacted adoption. In clinics that adopted
the innovation early or those that relied on champions, user
activity tended to plateau or decline, suggesting a negative
impact on sustainability.

In terms of working with the adopter system and organization,
our findings suggest a need to work with the context to integratea new patient-driven innovation in health care. In this respect,despite prosumerism, this patient-driven innovation is notdifferent from other innovations in health care [33]. Working
with a broader group of adopters, not just champions, andintegrating the patient-driven innovation with care processes
appeared to facilitate adoption. If context is not addressed bysuppliers, there is a risk that the perceived value of theinnovation will be influenced by the perceived complexity ofthe setting. This lived experience of complexity could not be
explained in terms of differences in the medical condition or
technology. Instead, it appeared to be more dependent on howcare processes had evolved and the human (in)ability to deal
with variation, uncertainty, complexity, and ambiguity ineveryday work life: complexity was in the eye of the beholder.

Perceived Complexity Influencing Adoption

Co-ownership and Trust

There was little variation between clinics regarding perceptionsof the condition and technology domains. Differences incomplexity were seen within the value proposition, adopter
system, organization, and embedding and adaptation domains.
The more complex these domains were perceived to be, thelower the level of adoption was.

When the app was first launched, the company took
responsibility for staff and patient education, with the intentionof having as little disturbance as possible in the clinics.
However, clinics that took shared responsibility for thepatient-driven rollout had higher and sustained adoption levels.
As studies in behavioral economics have demonstrated [41], ahigher level of perceived co-ownership leads to a higher
evaluation of the object in question. Co-ownership invites thestaff to learn and understand more about the patient-driven
innovation, which could explain why merely relying on

Organizations also demonstrated complexity; none of clinics
G-I had integrated the innovation into their clinical workflow,
and the innovation was used on an individual rather than on ateam basis, which put pressure on the single user (Multimedia
Appendix 3). This was reinforced by the special cause variation
observable in clinic G (consecutive points below/above thecenter line, months 69-83), where the activity dipped down
before shooting up and could be linked to a champion staff
member who led the adoption but left temporarily before
returning. The clinics saw funding as a barrier to implementing
new technologies, including the innovation. Clinic I mentioned
a severe resource pressure, including hiring stops (frozen posts),
which halted the use of the innovation, especially under the
stress of the pandemic. The embedding system was complex
due to the clinic’s inability to adapt the innovation use to criticaland unforeseen events (eg, the COVID-19 pandemic).

Discussion

The perceptions of value identified in this study demonstrate apatient-provider interdependency (ie, both the provider and thepatient must value and use the patient-driven innovation, or else
it will lead to a downward spiral of abandonment). As Floch ethttps://www.jmir.org/2024/1/e50527

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champions can be associated with lower adoption andresponsibility for patient training and research with higher
adoption.
Most clinics raised questions about the financial motives of thecompany behind the patient-driven innovation. Two clinics
questioned whether it is appropriate for them as medical
providers to “sell” a product from a private company.
Particularly, during the early rollout, the staff felt that pushinga premature version of the innovation on patients is a violationof their professional integrity. The distrust of a private businessovershadowed the patient-driven prosumer nature of theinnovation that should have engendered trust. This distrust could
be related to a commonly held negative view of privatizationin Sweden or may indicate that the company’s patient origins
had not been communicated clearly.

Utility of NASSS-CAT
This study used NASSS-CAT as a framework and tool toanalyze the implementation of a patient-driven innovation. We
found it helpful to characterize single domains but less suitable
to explore the interaction between domains. The framework isinnovation centric, which risks generating a bias that values
innovation per se regardless of its suitability for addressing thechallenge at hand or the purpose of the hosting clinic ororganization.
Another issue relates to the essence of the complexity captured
with CAT. Our findings support the basic tenet of NASSS-CAT
that adoption is inversely related to the level of complexity.
However, a closer look into the data suggests that what wascaptured may have been individuals’ perceptions of complexity
related to lived experiences rather than the actual contextual
complexity related to the level of interdependency. This
corroborates preliminary observations of the original CAT [29].
Thus, results may be more indicative of the maturity of thecomplexity mindset of individuals [42] rather than the contextual
complexity itself. To develop adoption strategies based on suchdata would be tantamount to developing treatment strategies
based on an incorrect diagnosis.
A more accurate assessment of contextual complexity could beachieved by exploring the level of agreement between
understanding the challenge and the proposed response[25,26,43]. These 2 questions are simpler to ask and evaluate
to generate actionable data: less agreement would suggest ahigher degree of complexity and therefore a need for strategies
that facilitate learning [43,44]. More direct implementation
works when things are simple (ie, greater agreement).

Strengths and Limitations
Directed content analysis inherently has some biases due to theuse of a preselected theory [39]. However, several measures
were taken to mitigate this limitation. For example, we used

Mazzocato et alopen-ended questions in the interview guide, and multiple
authors were involved in all the steps of the qualitative analysis.
There was variation in the number of participants per clinic,
which could have influenced the analysis. Clinics where theinnovation was perceived more positively and used to a greater
degree also tended to yield more interviews. This difference
may reflect both resource availability and the perceived valueof the innovation. Overall, the number of interview participants
was limited by the number of employees at each clinic and the
COVID-19 pandemic. The literature suggests that theoreticalsaturation is usually attained at around 12 interviews [45]. This
exceeds the number of staff members working with theinnovation in most of the clinics we studied. Despite our small
sample size, the participants’ specific knowledge about theinnovation and the care processes in each clinic contributed tostrengthening information power [46]. Moreover, thetriangulation of qualitative and quantitative data strengthened
the trustworthiness of the findings.
Not all clinics provided feedback on the process maps, although
all were given the opportunity. To further improve
trustworthiness, process maps were checked again against thetranscripts and reviewed by the developer’s clinic coordinator,
who had insight into the clinics and staff.
The total number of potential users was limited because theinnovation was first released in an iOS version only. As we did
not have data on the proportion of potential users who had an
iOS smartphone, we may have overestimated the number ofpotential users, in particular prior to the release of the Android
version.
This study did not examine patient outcomes related to theinnovation or satisfaction with the innovation. Interviewing
patients and informal caregivers would add valuable perspectives
to that of providers.

Conclusion
Patient-driven innovations could be highly relevant for healthcare, but their adoption has seldom been explored from theperspective of health care providers. We found that providers
play a significant role in the adoption of patient-driven
innovations in health care: patients cannot do it alone. Healthcare providers who make an effort to reduce the perceived
complexity in the adoption process, simplify their processes,
take co-ownership of the innovation, and work on its adoption
and improvement as a team, rather than relying on change
champions, improve their capability to support the adoption
and sustainability of innovative ideas developed by patients.
For patient-driven innovations to be adopted and sustained inhealth care, understanding patient-provider interdependency
and providers’ perspectives on what generates value is essential.

Acknowledgments
The authors acknowledge the contributions of Rafiq Muhammad to the statistical process control analyses and the valuable input
of Henna Hasson and other members of the Patient in the Driver’s Seat research program to the manuscript draft. A hallmark of
the research program is that the team, which includes patients and patient-innovators, has involved co-creative efforts throughout

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all aspects of the research process, including meta-level discussions on how our different perspectives influence the research
process.
This work was financially supported by the Swedish Research Council for Health, Working Life and Welfare (grant number
2018-01472). The funders had no involvement in the study design; in the collection, analysis, and interpretation of the data; inthe writing of the report; and in the decision to submit the paper for publication.

Data Availability
No additional data are available. The data sets generated and analyzed during this study are available from the corresponding
author upon reasonable request.

Authors' Contributions
CS, PM, and AH were responsible for conceptualization, with input from JLL, MMC, CW, JB, GO, and members of the Patientin the Driver’s Seat research program. The interview guide was developed by JLL, MMC, JB, CS, and PM. Data collection was
conducted by JLL and MMC, with support from CS. Qualitative data analysis was conducted by JLL, MMC, and CS. Statisticalprocess control (SPC) analysis was conducted and interpreted by JLL, MMC, PM, CS, and Rafiq Muhammad. JB and CW
provided input on the interpretation of the results. SPC graphs were compiled by JLL. The manuscript was drafted by CS, JLL,
MMC, JB, and PM. The manuscript was revised by CS, JLL, MMC, and PM, with input from JB, AH, CW, and GO. AH and JB
were involved in the revision of the manuscript and in the decision to submit the paper for publication. CW, CS, PM, and AH
were coapplicants of the funding grant, with Professor Henna Hasson as program lead. CS and PM served as principal investigators.

Conflicts of Interest
AH and JB are employees of Upstream Dream, the company that developed the innovation. Upstream Dream was not involvedin the study design but did facilitate data collection, and JB helped interpret the data based on knowledge of the participating CF
clinics. To mitigate bias, interviews and preliminary analysis were conducted without participation of the supplier. AH and JB
were interviewed, and their transcripts were subjected to the same data analysis methods, kept separate from the analyses of theother participants, and then used for clarification purposes for the statistical process control timeline analysis and complexity
analysis.

Multimedia Appendix 1
Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.
[DOCX File , 1243 KB-Multimedia Appendix 1]

Multimedia Appendix 2Interview guide.
[DOCX File , 37 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Nonadoption, abandonment, spread, scale-up, and sustainability (NASSS) domain areas and case-specific descriptions.
[PDF File (Adobe PDF File), 133 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Process maps.
[PDF File (Adobe PDF File), 707 KB-Multimedia Appendix 4]

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Abbreviations
CAT: complexity assessment tool
CF: cystic fibrosis
HCP: health care professional
LCL: lower control limit
mHealth: mobile health
NASSS: nonadoption, abandonment, spread, scale-up, and sustainability
RN: registered nurse
SPC: statistical process control
UCL: upper control limit

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Mazzocato et al

Edited by T de Azevedo Cardoso; submitted 04.07.23; peer-reviewed by D Rose, G Akrong; comments to author 06.03.24; revised
version received 08.04.24; accepted 20.06.24; published 31.07.24
Please cite as:
Mazzocato P, Luckhaus JL, Malmqvist Castillo M, Burnett J, Hager A, Oates G, Wannheden C, Savage C
A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case StudyJ Med Internet Res 2024;26:e50527
URL: https://www.jmir.org/2024/1/e50527
doi: 10.2196/50527
PMID: 39083342©Pamela Mazzocato, Jamie Linnea Luckhaus, Moa Malmqvist Castillo, Johan Burnett, Andreas Hager, Gabriela Oates, Carolina
Wannheden, Carl Savage. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.07.2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The
complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright andlicense information must be included.

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