Coverage and Utilization of Digital Healthcare Services: A Registry-Based Observational Study

Coverage and Utilization of Digital Healthcare Services: A Registry-Based Observational Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Coverage and Utilization of Digital Healthcare Services: A Registry-Based Observational Study View ORCID Profile Alexandra Dahlberg , View ORCID Profile Taavi Kaartinen , View ORCID Profile Sakari Jukarainen , View ORCID Profile Petja Orre doi: https://doi.org/10.1101/2025.11.06.25339651 Alexandra Dahlberg 1 Harjun terveys , Lahti, Finland 2 Faculty of Medicine, University of Helsinki , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexandra Dahlberg For correspondence: alexandra.dahlberg{at}harjunterveys.fi Taavi Kaartinen 2 Faculty of Medicine, University of Helsinki , Helsinki, Finland 3 Mehiläinen , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Taavi Kaartinen Sakari Jukarainen 2 Faculty of Medicine, University of Helsinki , Helsinki, Finland 3 Mehiläinen , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sakari Jukarainen Petja Orre 1 Harjun terveys , Lahti, Finland 3 Mehiläinen , Helsinki, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Petja Orre Abstract Full Text Info/History Metrics Preview PDF Abstract A retrospective, registry-based analysis describing the uptake, user characteristics, and clinical scope of a digital primary care clinic within the Finnish public health system. The study utilized data from 2,635,708 primary care encounters recorded between 2019 and 2024 to examine patterns of care following the digital clinic’s introduction. By 2024, digital visits accounted for 12.7% of all primary care encounters. Users of the digital clinic were significantly younger, more likely female, and had fewer comorbidities compared to those using traditional care, with notably low uptake among adults over 65. The most common conditions managed digitally were acute respiratory infections, dermatologic issues, and urinary tract infections. Follow-up analysis showed that 21% of patients had a new encounter within 30 days, with in-person visits being the most common subsequent modality. This study demonstrates that a digital clinic can expand access and resolve nearly half of cases without even a digital physician consultation, while also functioning as a triage system for cases requiring further care. However, targeted strategies are needed to increase adoption among older adults and individuals with chronic illnesses. Introduction Digital health platforms are transforming how patients access primary care. Asynchronous messaging and live chat, video visits, remote monitoring, patient portals, and digital care pathways create new routes to improve accessibility, timeliness, and operational efficiency 1 – 3 . In many countries, the COVID-19 pandemic accelerated the adoption of digital care models 4 , 5 , though interest in digital-first approaches predated the crisis 6 . For publicly funded health systems like Finland’s, where digital transformation is a strategic priority and equitable access remains a central policy objective, the implementation of these technologies requires thorough assessment to ensure they support accessibility, equity, and overall system efficiency. Economic evaluations suggest that digital-first pathways can be cost-effective in real- clinical settings 7 , 8 . However, understanding the demographic and clinical characteristics of digital care users is essential for designing inclusive and scalable digital health systems. Identifying patterns in utilization – such as differences by age, sex, or diagnosis – and examining changes in overall coverage over time can shed light on both the potential and limitations of digital modalities. For instance, studies in European contexts have shown that digital care users tend to be younger, more likely female, and more socioeconomically advantaged than the general patient population 9 , 10 . This study evaluates population coverage and real-world utilization. Using registry data from Harjun terveys in Finland, we examine the integration of a 24/7 digital primary care clinic within the publicly funded service model. Specifically, we assessed: (1) trends in overall primary care coverage; (2) the volume and proportion of digital visits and their follow-ups; (3) user characteristics across modalities, including age, sex, and comorbidity; and (4) the diagnostic spectrum of the digital encounters. Methodology Study design and setting We conducted a retrospective cohort study using routinely collected health service data from Harjun terveys in Päijät-Häme, Finland. The observation window spanned January 1, 2019, to December 31, 2024, providing two pre-implementation years (2019-2020) and up to four post-implementation years (2021-2024) following the Harjun terveys joint-venture model. System-wide digital services were introduced in January 2021, followed by the launch of a centralized digital clinic with a new operating model in January 2023 and the implementation of new software in April 2023. Finland provides universal healthcare coverage primarily through publicly funded services, with private providers in a complementary role. Three components operate in parallel: public, private, and occupational healthcare 11 . In some municipalities, parts of public service delivery are outsourced to private healthcare providers to address local capacity constraints, enhance service quality, and improve cost efficiency. Harjun terveys is a joint venture that delivers publicly funded primary health care, including outpatient medical and dental services, in the Päijät-Häme region of Finland, serving approximately 130,000 residents. Established in 2021 as a partnership between the regional wellbeing authority and a private healthcare company, the venture was created to address insufficient local capacity to meet patient demand. In addition to expanding service coverage, Harjun terveys implemented a multidisciplinary team-based model and a “digi-physical” service pathway. All residents can access a 24/7 digital clinic via the Päijät-Sote application, which provides chat-based consultations with nurses and physicians alongside traditional telephone and in- person appointments. The model is intended to reduce appointment delays and improve access. All primary care encounters – whether a physical visit at a health center, a telephone triage, or an asynchronous chat consultation via the app – are documented in the national unified electronic health record (EHR) system. The digital clinic is staffed by nurses and general practitioners (GPs) who provide consultation, prescriptions, and referrals through chat messaging, with the ability to escalate patients to physical appointments when needed. Harjun terveys serves an urban-rural mixed population, with a slightly older age distribution than the national average 12 . Data Extraction We obtained pseudonymized encounter-level data. For each primary care episode of care, we extracted: patient demographics (year of birth, sex, and home municipality), encounter modality, dates and times of encounters, provider type (nurse or physician), and diagnosis codes. Chronic conditions (e.g., diabetes, ischemic heart disease, asthma/COPD) were identified using prior diagnostic history for the studied period. Each patient’s Charlson Comorbidity Index (CCI) was calculated using an adapted algorithm for ICD-10 code mapping 13 . Primary outcome measures included service coverage and utilization patterns. Service coverage was defined as the proportion of the population (Päijät-Häme residents served by Harjun terveys) with at least one primary care encounter during a year, and utilization as encounters per 1,000 residents. A primary care encounter was defined as any encounter with a nurse or a general practitioner at a primary care facility, for example the digital clinic, or a GP office, excluding encounters related to health promotion such as vaccinations and maternity or child preventative health clinic visits. Coverage was stratified by modality to assess engagement via digital versus traditional channels. Population denominators were drawn from national population data 12 . Digital service utilization was analyzed by calculating the number and share of unique digital users annually, alongside the total number of digital encounters. Digital users were defined as individuals with at least one digital clinic encounter during the studied year; non-digital users were those who did not use the digital clinic. To characterize the clinical scope of digital consultations, we examined the most frequent diagnoses recorded in digital clinic encounters during 2023-2024. Diagnoses were analyzed using both ICPC-2 codes, which reflect presenting symptoms and primary care-level assessments, and ICD-10 codes, which were used when patients were escalated to a physician for diagnosis or prescription. Statistical Analysis We present proportions, means (with standard deviations), or medians (with interquartile ranges) where appropriate. Differences in demographic characteristics between digital users and non-users were tested using χ ² tests for categorical variables and t -tests for continuous variables. All statistical tests were two-tailed with a significance level of P <.05. Data analyses were performed using Python 14 – 18 . The study was approved by the Päijät-Häme Wellbeing Services County for secondary use of health data (decision number HA/187/07.01.04.05/2025). This study was based on secondary use of pseudonymized administrative data. In accordance with the Finnish Act on the Secondary Use of Health and Social Data (552/2019) 19 and the research permit, individual patient consent or separate ethical review was not required. Results The initial extract comprised 2,706,347 primary care encounters from 1 January 2019 through 31 December 2024 (inclusive). After deduplication, exclusion of encounters with unknown profession codes or out-of-scope professional categories, and removal of records with missing date of birth, 2,635,708 encounters remained for analysis. This represents a net reduction of 70,639 records, or 2.61%. Primary Care Coverage and Service Utilization We examined trends in primary care coverage and service utilization in the Päijät- Häme region from 2019 to 2024, a period that encompasses the introduction of digital services and the subsequent implementation of a new centralized operational model for the digital clinic. For a comprehensive interpretation of the findings, it is crucial to contextualize the state of healthcare services prior to the intervention. Upon the initiation of the joint venture, the services provided by the local municipal authorities had been considered suboptimal for a prolonged period, particularly in terms of access to care and availability. Consequently, a significant portion of the subsequent increase in service utilization may be attributed to broad enhancements aimed at better meeting the population’s healthcare needs, rather than being a direct outcome of the digital clinic’s introduction. Annual coverage increased gradually from 25.3% in 2019 to 28.3% in 2024 ( Table 1 ). Over the same period, total utilization rose from 1,473 to 1,886 encounters per 1,000 residents. The uptick in utilization accelerated after 2021 and continued through 2024. Because the centralized digital clinic began on 1 January 2023, changes observed in 2021-2022 are partly interpreted as part of an ongoing system transition rather than effects of the centralized clinic. View this table: View inline View popup Download powerpoint Table 1. Annual primary care coverage (%) and encounters per 1,000 population, 2019–2024. Digital share of the encounters. Coverage based on national population data 12 . Digital encounters expanded quickly, accounting for 12.7% of all recorded interactions in 2024. In-person visits remained the predominant modality but declined in relative terms, while phone contacts were stable. Conversely, among older adults, particularly those over 75, digital care did not substantially expand reach as seen in Figure 1 . Coverage in this group remained near pre-pandemic levels, highlighting a potential digital divide in access or preferences for traditional modes of care. Download figure Open in new tab Figure 1 Health service coverage in the area by age distribution. Proportion of residents in each age group with at least one healthcare encounter during the year. The gray line indicates the difference in coverage between areas with and without access to digital health services in 2024. Table 1 and Figure 1 together highlight a consistent pattern: the introduction of digital primary care improved service reach, particularly among digitally literate and younger populations. Demographic Profile of Digital Service Users By 2024, digital health services had been adopted across a broad range of age groups, though uptake remained uneven. Younger adults were most likely to engage with the digital clinic, particularly those aged 20 to 39 years. Uptake declined progressively in older age brackets, with notably lower participation among those aged 65 and above. As shown in Table 2 , digital service users were substantially younger than those using traditional modalities. This was reflected in both mean and median age comparisons, with statistically significant differences observed across years. Women were more likely than men to access digital services. View this table: View inline View popup Download powerpoint Table 2 Demographic characteristics of digital and traditional service users in 2023 and 2024 . Comparison of age and sex distribution between users of digital clinic services and those utilizing traditional encounter types. CCI stands for the Charlson comorbidity index. P-values for age are calculated using a two-sided t-test; sex distribution and users with CCI scores are compared using the chi-square test. Users of the digital clinic also differed clinically. A smaller proportion of digital users documented chronic conditions compared to those using traditional encounters. This suggests a preference or suitability of digital modalities for relatively healthier patients or those with less complex care needs. These patterns were consistent with the previous year’s comparison and were statistically significant based on chi-square analysis. Figure 2 illustrates the distribution of encounter types by age group. For each encounter modality – digital, phone, and in-person – only a single encounter per person was counted. This allows for a clearer interpretation of service penetration within each age band. Physical visits and phone calls were most common among the oldest age groups, whereas digital usage peaked in the groups for early adulthood and declined thereafter. Download figure Open in new tab Figure 2 Age distribution of encounter types in 2024. Proportion of the population with at least one healthcare encounter in 2024, by age group and encounter type. Encounter types include digital clinics, physical visits, and phone calls. Proportions are calculated using unique individuals per age group and encounter type, relative to the age-matched population. Only one encounter per person per type is counted. Digital Consultation Characteristics: Diagnoses and Conditions Managed Figure 3 displays the 20 most common ICPC-2 codes recorded in the digital clinic, collectively representing 48.2% of all interactions. The most common reason for contact was respiratory infections (R74 and R83, 13.0%). Other frequent codes included general symptom consultations such as health maintenance or preventive advice (−45 and A98, 8.1%), various skin symptoms (S29 and S21, 5.0%), and throat or eye-related complaints (R21, F03, and F29, 6.8%). Notably, administrative interactions related to prescription renewal also featured (−50, 2.7%), indicating that digital services were used not only for acute care but also for preventive and administrative functions. Download figure Open in new tab Figure 3. Most common ICPC-2 diagnosis categories in the digital clinic (2023–2024). The 20 most frequent ICPC-2 codes recorded in digital clinic consultations, shown as the percentage of all digital clinic encounters. Figure 4 summarizes the 20 most frequent ICD-10 diagnoses among patients escalated to the digital clinics physician-level care. The most common conditions were prescription renewal requests (Z76.0, 1.3%), acute cystitis (N30.0, 1.2%), and conjunctival infections (H10.x series, 1.7%). Additional diagnoses reflected a typical profile of low-complexity primary care issues, including cellulitis, dermatitis, and streptococcal pharyngitis. These findings suggest that patients referred to digital consultations with physicians typically require evaluation for infections or dermatological conditions, often with a prescription. Download figure Open in new tab Figure 4. Most common ICD-10 diagnosis categories in the digital clinic (2023-2024). Top 10 ICD-10 codes recorded during digital consultations with a physician, following escalation from the nurse encounter at the digital clinic. Percentages represent the share of all digital clinic encounters. Together, these distributions highlight the dual clinical role of the digital clinic: managing common acute complaints directly, and efficiently triaging cases that require further care. The high frequency of minor infections and symptom-based codes further supports the interpretation that digital consultations are primarily used for low-acuity, high-frequency presentations that benefit from rapid, accessible care. Follow-up Analysis To evaluate care continuity following digital nurse consultations, we analyzed subsequent patient encounters over a 30-day period. The cohort included 86,258 digital nurse encounters. On the same day as the initial digital encounter, 24.7% (n = 21,336) involved a digital consultation with a physician, while 14.4% (n = 12,397) resulted in a new appointment being booked. To isolate unplanned or deferred care needs as best as possible, we excluded follow- up encounters that had already been booked at the time of the digital interaction. The analysis therefore focused exclusively on the first new encounter occurring more than 24 hours after the digital consultation. Across all follow-up modalities, 5.0% of patients had a new encounter within 48 hours, 14.9% within 14 days, and 21.0% within 30 days. Figure 5 illustrates the cumulative incidence of follow-up activity over the 30-day period, stratified by encounter type. Physical visits represented the most common form of follow-up, with over 10% of patients receiving in-person care within 30 days. Phone-based interactions, digital revisits, and other administrative or triage-related encounters accounted for additional follow-up activity. Download figure Open in new tab Figure 5 Cumulative follow-up by encounter type over 30 days following an encounter with the digital clinic. Follow-ups that had been booked prior to the encounter with the digital clinic are excluded. Dashed lines indicate follow-up encounters that were scheduled on the same day as the digital clinic visit. To examine planned follow-up, we evaluated encounters that were booked on the same day as the digital nurse consultation but scheduled for a later date. The likelihood of such pre-booking varied substantially by encounter type. Among patients who had a follow-up within 30 days, 39% of phone calls were booked during the initial digital consultation, compared to 17% of physical visits and 11% of other encounter types. For these pre-booked follow-ups, the most common presenting reasons included naevus/mole, laceration/cut, acute upper respiratory infections, diabetes, sleep disturbances and more general preventive or health maintenance codes. These are conditions for which a subsequent follow-up call or in-person evaluation is often clinically warranted. To illustrate follow-up care utilization, Figure 6 presents a Sankey diagram of healthcare utilization after a digital clinic visit. Flow widths correspond to the share of patients at each step. For readability, paths are limited to three steps, and the horizon is 14 days. The diagram takes into account all healthcare utilization, planned and un- planned. Nearly half of trajectories terminate at the nurse-led digital clinic visit, 14% proceed to a physician consultation, and 14% culminate in an in-person visit, with smaller fractions continuing via phone contacts or administrative interactions. Download figure Open in new tab Figure 6 Sankey diagram of healthcare utilization after a digital clinic visit. Flow widths correspond to the proportion of patients at each step. The diagram covers the first 14 days after the visit, and sequences are truncated at three steps to aid interpretation. Discussion This study assessed a digital-first primary care service embedded in a Finnish public system and additionally reported primary healthcare coverage to inform equity considerations. Following the implementation of the digital clinic, overall healthcare coverage expanded, with the digital clinic contributing to 12.7% of all annual primary care encounters in the area in 2024. Because implementation coincided with other joint-venture changes, the relative contribution of the digital channel to the coverage increase cannot be isolated with the present design. Interpretation should therefore be conservative with respect to attribution. The demographic analysis showed higher adoption among younger adults and women, consistent with findings from other settings 9 , 10 . These groups were more likely to engage with the chat-based platform, while older adults and individuals with multiple chronic conditions continued to rely primarily on traditional care. While lower engagement among older patients could reflect usability barriers 20 , preferences 21 , 22 or differences in clinical appropriateness, it raises important equity concerns. Strategies such as assisted digital onboarding or targeting digital support to specific pathways for chronic conditions may improve inclusion. Most digital encounters were managed entirely online, with approximately 85.6% resolved without a need to book a new appointment. Across all follow-up modalities, 5.0% of patients had a new encounter within 48 hours, 14.9% within 14 days, and 21.0% within 30 days, excluding those booked prior to the digital encounter. These resolution rates reflect the appropriateness of the digital modality for managing acute, low-complexity conditions, which constituted the bulk of digital encounters. Our findings are consistent with previous studies such as Glock et al. 10 , who found that text-based consultations were suited for uncomplicated, low-complexity presentations. They found that about one-quarter of Swedish eVisit patients required in-person follow- up within 14 days (for the same diagnostic group), which was higher than the 15% observed in our cohort. Usage patterns of the digital clinic also revealed that most interactions were episodic rather than part of chronic care management. Few high-frequency or chronic patients used the digital clinic for their routine follow-up needs, although a small number used it for incidental concerns. Expanding the scope of digital services to include selected chronic care tasks – such as remote monitoring or asynchronous prescription follow- up – may represent an area for future innovation 23 . Similarly, digital utilization for mental health-related concerns was also modest, despite the potential of digital platforms to lower access barriers. Prior research suggests this may reflect limitations in perceived personal connection, lack of trust, and concerns about privacy and confidentiality in digital encounters 24 , 25 . Finland’s Finnish Therapy Navigator, created through a government-funded program and implemented in the digital clinic, offers an example of emerging tools that could enhance digital pathway guidance and uptake 26 . Ensuring equitable access to digital health requires deliberate design and implementation. Evidence indicates that digital services can improve access for specific populations 27 , 28 . The corresponding policy question is national in scope: what minimum level of care every public provider should be required to deliver, and through which modalities. In line with previous recommendations 29 – 32 , hybrid models that preserve physical care options, improve users’ digital skills, and offer personalized support (including remote digital assistance) are crucial. Equally important is the development of user-centered platforms with intuitive design and clear guidance for patients and clinicans 20 , 32 . Finally, while we did not conduct a formal cost analysis in this study, prior work in the same setting has demonstrated significant cost reductions for digitally initiated episodes when compared to matched traditional care episodes 8 . Taken together, these findings suggest that digital-first primary care, when carefully implemented and fully integrated into the broader health system, can help to improve access and streamline service use. Future research should continue to explore long-term outcomes, equity impacts, and patient experiences to ensure that digital transformation enhances both efficiency and inclusiveness in healthcare delivery. Limitations This study relied on registry healthcare data, which has inherent limitations. Some outcomes like clinical resolution or patient satisfaction were not directly measured; we inferred resolution from follow-up rates, which could miss cases where patients sought care outside the system under study. There is also potential misclassification in diagnostic coding. Since this was an observational study within one wellbeing services county, generalizability may be limited; factors unique to Päijät-Häme (such as the specific app used or population characteristics) might affect results. Additionally, our comparisons between digital and traditional users are subject to confounding – the groups differ by age and health status by nature. Attribution should be cautious, concurrent system improvements under the joint venture mean that attributing all observed changes to the digital channel risks overstatement. As a descriptive study, we cannot establish causality. Conclusion The integration of a digital primary care clinic in the Harjun terveys service network has significantly influenced healthcare coverage and utilization patterns in Päijät-Häme, Finland. Digital health services via the Päijät-Sote app have expanded access to care, enabling a notable portion of the population – especially younger adults – to receive timely care for acute issues without needing physical visits. The digital-first model achieved high resolution rates for uncomplicated conditions, and most patients did not require additional in-person follow-up, indicating effective management. We observed distinct user demographics, with digital services attracting younger and relatively healthier patients, while older and chronically ill patients continued to use traditional care channels. Common conditions like skin ailments, respiratory infections, and minor urinary issues were handled efficiently online, whereas more complex concerns were not as frequent in the digital clinic. Our findings suggest that a well-implemented digital clinic can serve as a complementary extension of primary care. However, to ensure that the benefits are broadly shared, strategies to engage under-represented groups in digital care are needed, as is ongoing monitoring of quality and outcomes. As health systems worldwide continue to adopt digital health innovations, the Harjun terveys experience provides encouraging evidence that access can be widened, and care streamlined. Future research and quality improvement efforts will focus on refining the model – for example, through patient education, interface improvements, and possibly expanding digital offerings to chronic care – and rigorously evaluating long-term health outcomes in this digital-enhanced primary care paradigm. Declaration statements Data Availability All data generated or analyzed during this study are presented within the published article. Individual-level patient data are not publicly available due to privacy and data protection regulations. Code Availability The underlying code for this study is available at https://github.com/achdahlberg/coverage-utilization-digital-clinic-2025 . Authors’ Contributions A.D. was responsible for the study’s conceptual framework and design, performed the data analysis, and prepared the first draft of the manuscript. S.J. oversaw the research, offered methodological support, and provided critical revisions to the manuscript. T.K. supported the development of the digital health context, advised on data organization, and contributed to manuscript refinement. P.O. offered strategic guidance during the study design phase, enabled access to key data sources, and assisted in manuscript development. All authors participated in the interpretation of the results and approved the final manuscript for submission. Competing Interests The authors disclose the following potential conflicts of interest: All authors are employees of Mehiläinen, the healthcare organization that developed and operates the digital clinic assessed in this study. Some authors also hold personal financial interests in the company. The study received funding from Mehiläinen, and institutional support was provided by the leadership of Harjun terveys throughout the research process. Acknowledgements The authors gratefully acknowledge the support provided by Mehiläinen Oy, Harjun terveys oy, and the Päijät-Häme Wellbeing Services County. ChatGPT 33 was used to assist in improving the clarity and coherence of the manuscript and to support routine programming tasks; it was not used for conceptual input, interpretation of findings, or generation of original content. All content was thoroughly reviewed, edited, and approved by the authors, who take full responsibility for the accuracy and integrity of the final work. Footnotes Tables and figures placed in the body of the text, where they are referenced. References ↵ Dorsey , E. R. & Topol Eric , J. State of Telehealth . New England Journal of Medicine 375 , 154 – 161 ( 2016 ). doi: 10.1056/NEJMra1601705 OpenUrl CrossRef PubMed De Guzman , K. R. , Snoswell , C. L. , Caffery , L. J. & Smith , A. C . Economic evaluations of videoconference and telephone consultations in primary care: A systematic review . Journal of Telemedicine and Telecare 30 , 3 – 17 ( 2021 ). doi: 10.1177/1357633X211043380 OpenUrl CrossRef PubMed ↵ Campbell , K. et al. The Impact of Virtual Consultations on the Quality of Primary Care: Systematic Review . J Med Internet Res 25 , e48920 ( 2023 ). doi: 10.2196/48920 OpenUrl CrossRef PubMed ↵ Neves , A. L. & Burgers , J . Digital technologies in primary care: Implications for patient care and future research . Eur J Gen Pract 28 , 203 – 208 ( 2022 ). doi: 10.1080/13814788.2022.2052041 OpenUrl CrossRef ↵ Ward , K. , Vagholkar , S. , Sakur , F. , Khatri , N. N. & Lau , A. Y. S . Visit Types in Primary Care With Telehealth Use During the COVID-19 Pandemic: Systematic Review . JMIR Med Inform 10 , e40469 ( 2022 ). doi: 10.2196/40469 OpenUrl CrossRef ↵ Ministry of Social Affairs and Health . ( 2023 ). ↵ Gentili , A. et al. The cost-effectiveness of digital health interventions: A systematic review of the literature . Frontiers in Public Health 10 ( 2022 ). doi: 10.3389/fpubh.2022.787135 OpenUrl CrossRef PubMed ↵ Dahlberg , A. , Jukarainen , S. , Kaartinen , T. & Orre , P . Cost minimization analysis of digital-first healthcare pathways in primary care . npj Digital Medicine 8 , 546 ( 2025 ). doi: 10.1038/s41746-025-01937-z OpenUrl CrossRef PubMed ↵ Darley , S. et al. Understanding How the Design and Implementation of Online Consultations Affect Primary Care Quality: Systematic Review of Evidence With Recommendations for Designers, Providers, and Researchers . J Med Internet Res 24 , e37436 ( 2022 ). doi: 10.2196/37436 OpenUrl CrossRef PubMed ↵ Glock , H. et al. eVisits to primary care and subsequent health care contacts: a register-based study . BMC Primary Care 25 , 297 ( 2024 ). doi: 10.1186/s12875-024-02541-y OpenUrl CrossRef ↵ Eskola , P. , Tuompo , W. , Riekki , M. , Timonen , M. , & Auvinen , J.. (ed Finnish Ministry of Social Affairs and Health ) ( Institutional Repository for the Government , 2022 ). ↵ Official Statistics of Finland . Population structure , ( 2025 ). ↵ Brusselaers , N. & Lagergren , J . The Charlson Comorbidity Index in Registry-based Research . Methods Inf Med 56 , 401 – 406 ( 2017 ). doi: 10.3414/me17-01-0051 OpenUrl CrossRef PubMed ↵ Harris , C. R. et al. Array programming with NumPy . Nature 585 , 357 – 362 ( 2020 ). doi: 10.1038/s41586-020-2649-2 OpenUrl CrossRef PubMed pandas-dev/pandas: Pandas v. latest (Zenodo, 2020 ). Hunter , J. D . Matplotlib: A 2D Graphics Environment . Computing in Science & Engineering 9 , 90 – 95 ( 2007 ). doi: 10.1109/MCSE.2007.55 OpenUrl CrossRef PubMed Waskom , M . L. seaborn: statistical data visualization . Journal of Open Source Software 6 , 3021 ( 2021 ). doi: 10.21105/joss.03021 OpenUrl CrossRef ↵ Virtanen , P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python . Nature Methods 17 , 261 – 272 ( 2020 ). doi: 10.1038/s41592-019-0686-2 OpenUrl CrossRef PubMed ↵ Act on the Secondary Use of Health and Social Data , (552/2019). ↵ Kaihlanen , A.-M. et al. Towards digital health equity - a qualitative study of the challenges experienced by vulnerable groups in using digital health services in the COVID-19 era . BMC Health Services Research 22 , 188 ( 2022 ). doi: 10.1186/s12913-022-07584-4 OpenUrl CrossRef PubMed ↵ Mangin , D. et al. Multimorbidity, eHealth and implications for equity: a cross-sectional survey of patient perspectives on eHealth . BMJ Open 9 , e023731 ( 2019 ). doi: 10.1136/bmjopen-2018-023731 OpenUrl Abstract / FREE Full Text ↵ Frishammar , J. , Essén , A. , Bergström , F. & Ekman , T . Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them . Technological Forecasting and Social Change 189 , 122319 ( 2023 ). doi: 10.1016/j.techfore.2023.122319 OpenUrl CrossRef ↵ Melchiorre , M. G. et al. eHealth in integrated care programs for people with multimorbidity in Europe: Insights from the ICARE4EU project . Health Policy 122 , 53 – 63 ( 2018 ). doi: 10.1016/j.healthpol.2017.08.006 OpenUrl CrossRef PubMed ↵ Berry , N. , Lobban , F. & Bucci , S . A qualitative exploration of service user views about using digital health interventions for self-management in severe mental health problems . BMC Psychiatry 19 , 35 ( 2019 ). doi: 10.1186/s12888-018-1979-1 OpenUrl CrossRef PubMed ↵ Liberati , E. et al. Remote care for mental health: qualitative study with service users, carers and staff during the COVID-19 pandemic . BMJ Open 11 , e049210 ( 2021 ). doi: 10.1136/bmjopen-2021-049210 OpenUrl Abstract / FREE Full Text ↵ Saarni , S. et al. The Finnish Therapy Navigator – Digital support system for introducing stepped care in Finland . Psychiatria Fennica 53 , 120 – 137 ( 2022 ). OpenUrl ↵ Erku , D. et al. Digital Health Interventions to Improve Access to and Quality of Primary Health Care Services: A Scoping Review . Int J Environ Res Public Health 20 ( 2023 ). doi: 10.3390/ijerph20196854 OpenUrl CrossRef ↵ Talwar , G. & Sinha , S . How are digital platforms affecting the accessibility of healthcare services? Health Services Management Research , 09514848251358328 ( 2025 ). doi: 10.1177/09514848251358328 OpenUrl CrossRef ↵ Beaunoyer , E. , Dupéré , S. & Guitton , M. J . COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies . Computers in Human Behavior 111 , 106424 ( 2020 ). doi: 10.1016/j.chb.2020.106424 OpenUrl CrossRef Nguyen , M. H. , Hargittai , E. & Marler , W . Digital inequality in communication during a time of physical distancing: The case of COVID-19 . Computers in Human Behavior 120 , 106717 ( 2021 ). doi: 10.1016/j.chb.2021.106717 OpenUrl CrossRef PubMed Xie , B. et al. When Going Digital Becomes a Necessity: Ensuring Older Adults’ Needs for Information, Services, and Social Inclusion During COVID-19 . Journal of Aging & Social Policy 32 , 460 – 470 ( 2020 ). doi: 10.1080/08959420.2020.1771237 OpenUrl CrossRef PubMed ↵ Wilson , S. et al. Recommendations to advance digital health equity: a systematic review of qualitative studies . npj Digital Medicine 7 , 173 ( 2024 ). doi: 10.1038/s41746-024-01177-7 OpenUrl CrossRef PubMed ↵ OpenAI . ChatGPT [Large language model]. ( 2025 ). View the discussion thread. Back to top Previous Next Posted November 07, 2025. Download PDF Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Coverage and Utilization of Digital Healthcare Services: A Registry-Based Observational Study Alexandra Dahlberg , Taavi Kaartinen , Sakari Jukarainen , Petja Orre medRxiv 2025.11.06.25339651; doi: https://doi.org/10.1101/2025.11.06.25339651 Share This Article: Copy Citation Tools Coverage and Utilization of Digital Healthcare Services: A Registry-Based Observational Study Alexandra Dahlberg , Taavi Kaartinen , Sakari Jukarainen , Petja Orre medRxiv 2025.11.06.25339651; doi: https://doi.org/10.1101/2025.11.06.25339651 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Health Systems and Quality Improvement Subject Areas All Articles Addiction Medicine (566) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6575) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5423) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fec139b9a408650',t:'MTc3OTI4NzgxNw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();