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Osteoarthritis is a disabling condition that is often associated with other comorbidities. Total hip or knee arthroplasty is an effective surgical treatment for osteoarthritis when indicated, but comorbidities can impair their results by increasing complications and social and economic costs. Integrated care (IC) models supported by eHealth can increase efficiency through defragmentation of care and promote patient-centeredness.
This study aims to assess the effectiveness and cost-effectiveness of implementing a mobile health (mHealth)–enabled IC model for complex chronic patients undergoing primary total hip or knee arthroplasty.
As part of the Horizon 2020 Personalized Connected Care for Complex Chronic Patients (CONNECARE) project, a prospective, pragmatic, two-arm, parallel implementation trial was conducted in the rural region of Lleida, Catalonia, Spain. For 3 months, complex chronic patients undergoing total hip or knee arthroplasty and their caregivers received the combined benefits of the CONNECARE organizational IC model and the eHealth platform supporting it, consisting of a patient self-management app, a set of integrated sensors, and a web-based platform connecting professionals from different settings, or usual care (UC). We assessed changes in health status (12-item short-form survey [SF-12]), unplanned visits and admissions during a 6-month follow-up, and the incremental cost-effectiveness ratio.
A total of 29 patients were recruited for the mHealth-enabled IC arm, and 30 patients were recruited for the UC arm. Both groups were statistically comparable for baseline characteristics, such as age; sex; type of arthroplasty; and Charlson index, American Society of Anesthesiologists classification, Barthel index, Hospital Anxiety and Depression scale, Western Ontario and McMaster Universities Osteoarthritis Index, and Pfeiffer mental status questionnaire scores. Patients in both groups had significant increases in the SF-12 physical domain and total SF-12 score, but differences in differences between the groups were not statistically significant. IC patients had 50% fewer unplanned visits (
Chronic patients undergoing hip or knee arthroplasty can benefit from the implementation of patient-centered mHealth-enabled IC models aimed at empowering patients and facilitating transitions from specialized hospital care to primary care. Such models can reduce unplanned contacts with the health system and reduce overall health costs, proving to be cost-effective. Overall, our findings support the notion of system-wide cross-organizational care pathways supported by mHealth as a successful way to implement IC for patients undergoing elective surgery.
The progressive aging of populations has led to an increased burden of chronic diseases [
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are effective surgical treatments for end-stage OA, improving joint function and health-related quality of life (QoL) [
So far, the strategies aiming to improve the outcomes of elective surgeries have mainly focused on enhanced recovery protocols, prehabilitation, and postoperative rehabilitation protocols, which have been proven effective for lower limb arthroplasty [
The CONNECARE project is a European Union Horizon 2020 Research and Innovation project aiming to co-design, develop, deploy, and evaluate a novel smart and adaptive organizational IC model for complex chronic patients (CCPs) [
As part of the CONNECARE project, a novel mHealth-enabled IC model was implemented in Lleida, Spain, targeting older CCPs undergoing elective THA or TKA. The existing care model for THA and TKA in Lleida is an enhanced recovery after surgery (ERAS) pathway based on different interventions to reduce perioperative stress; maintain and support homeostasis and physiological function; and accelerate the achievement of discharge criteria, including minimizing complications and readmission [
This paper describes the results in terms of effectiveness and cost-effectiveness of the implementation of an mHealth-enabled IC model for the prevention of hospital readmissions in CCPs undergoing THA and TKA.
This was a prospective, pragmatic, two-arm, parallel implementation trial comparing usual care (UC) with a 3-month mHealth-enabled IC intervention. The study was conducted from July 2018 to August 2019 at the University Hospital of Santa Maria (Lleida, Spain) and its network of primary care centers. This corresponds to a large rural area accounting for more than 236,000 citizens with a life expectancy of 80 and 86 years for men and women, respectively [
The eligibility criteria were home-dwelling patients elected for primary THA or TKA at the University Hospital of Santa Maria; aged >65 years; being defined as CCP (Charlson index score ≥3, taking four or more pills per day, and having had contact with the health system at least two times in the last 12 months); being classified according to the American Society of Anesthesiologists (ASA) classification as ASA II (mild systemic disease) or ASA III (severe systemic disease); and successfully passing a basic technological test, aimed to ensure the availability of internet connection at home as well as patients’ or caregivers’ competence with the use of a smartphone, tablet, or computer. The basic technological test can be found in
The recruitment was done in several waves to match the pace of the CONNECARE project technological developments. In each wave, consecutive potential participants were contacted by a case manager during preoperative assessment at the anesthesiology outpatient clinic. The case manager explained the study protocol and obtained informed consent. These patients formed the intervention arm. After the recruitment of each patient included in the intervention arm, an active search for a control with similar characteristics from the surgery waitlist of the Orthopedics Department of University Hospital of Santa Maria began. This enhanced the similarity of patients in the intervention and control arms, although it implied a certain lag in the recruitment of controls (from some days to few weeks). All patients and their caregivers, regardless of study arm, received a face-to-face explanation about the study and provided informed consent.
Patients in the intervention arm were attended using an IC model, including (1) preliminary assessment of the patient's health status using several questionnaires, tests, and indices specific to their main chronic diseases and social needs; (2) a self-management app, with status and performance reports, a virtual coach with customizable automated feedback, and full communication with the care team; (3) a Fitbit Flex 2 digital activity tracker [
Variables characterizing the patients were collected at recruitment using the SACM on tablet and/or desktop computers. Collected variables included main baseline characteristics, such as age, sex, main chronic diseases, Charlson index of comorbidities [
The cost estimation of the IC program and used health care resources is described in
The main outcomes were (1) intervention effectiveness, as measured by the changes in the 12-item short-form survey (SF-12) health questionnaire’s physical and mental domains (baseline vs discharge) [
Participants’ baseline characteristics were described by n (%), mean (SD), or median (P25-P75), as appropriate. Comparisons between IC and control patients’ baseline characteristics were performed using the chi-square test,
This study was approved by the Ethics Committee of Hospital Arnau de Vilanova (CEIC-1685), and all patients provided written informed consent. All collected data were handled and stored in accordance with the current national and international legislation.
Up to 82 patients were screened for eligibility. Of them, 49% (40/82) failed the technological test because they did not have an internet connection and 4% (3/82) refused to participate. Therefore, 39 patients were recruited for the mHealth-enabled IC arm and 30 for the UC arm. Final analyses were based on 29 IC and 30 control patients who completed the follow-up (
Study flowchart. EMR: electronic medical record.
The baseline characteristics of the patients are presented in
Baseline characteristics of the patients in the usual care and integrated care arms (N=59).
Characteristic | Usual care (n=30) | Integrated care (n=29) | ||||||
Sex (male), n (%) | 8 (27) | 12 (41) | .23 | |||||
Age (years), mean (SD) | 74 (8) | 72 (9) | .46 | |||||
Charlson score, mean (SD) | 4.3 (1.7) | 4.2 (1.5) | .81 | |||||
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.52 | |||||||
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II | 10 (33) | 12 (41) |
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III | 20 (67) | 17 (59) |
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Pain | 9.5 (3.4) | 9.6 (3.8) | .93 | ||||
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Function | 38.7 (12.8) | 39.8 (14.2) | .77 | ||||
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Stiffness | 2.3 (2.1) | 3.0 (2.8) | .29 | ||||
Barthel score, median (IQR) | 95 (90-100) | 100 (95-100) | .16 | |||||
HADb scale anxiety score, mean (SD) | 6.2 (4.9) | 5.0 (3.9) | .34 | |||||
HAD scale depression score, mean (SD) | 5.0 (2.3) | 5.1 (2.9) | .88 | |||||
Pfeiffer intact intellectual functioning, n (%) | 30 (100) | 27 (93) | .14 | |||||
Surgery location: knee, n (%) | 22 (73) | 26 (90) | .11 |
aChi-square test,
bHAD: Hospital Anxiety and Depression.
Changes in health status in the usual care and integrated care arms.
12-item short-form survey score | Baseline, mean (SD) | Discharge, mean (SD) | Change, mean (SD) | ||
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UCa | 27.9 (6.4) | 42.0 (7.7) | 14.1 (9.0) | <.001b |
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ICc | 29.9 (10.0) | 45.3 (9.8) | 15.4 (11.7) | <.001b |
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Difference | 2.0 (2.2) | 3.3 (2.2) | 1.4 (3.2) | .79d |
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UC | 48.1 (14.2) | 50.2 (13.5) | 2.0 (11.9) | .35b |
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IC | 52.1 (14.2) | 52.8 (12.9) | 0.8 (15.2) | .79b |
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Difference | 3.9 (3.6) | 2.6 (3.6) | −1.3 (5.1) | .94d |
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UC | 76.1 (15.6) | 92.2 (18.1) | 16.1 (14.8) | <.001b |
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IC | 81.9 (18.8) | 98.1 (15.6) | 16.2 (14.3) | <.001b |
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Difference | 5.9 (4.5) | 6.0 (4.5) | 0.1 (6.3) | .94d |
aUC: usual care.
bPaired
cIC: integrated care.
dLinear regression predicting the difference in the changes experienced by each arm, adjusted by age, sex, and Charlson index.
Total use of health services during the follow-up period (N=59).
Health service | Usual care (n=30), mean (SD) | Integrated care (n=29), mean (SD) | Adjusted |
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All unplanned visits | 1.4 (1.5) | 0.7 (1.0) | .03 | .006 |
Unplanned visits directly related to the surgery procedure | 0.8 (1.2) | 0.4 (0.7) | .10 | .02 |
All hospital admissions | 0.03 (0.2) | 0 (0) | N/Ac | N/A |
Hospital admissions directly related to the surgery procedure | 0.03 (0.2) | 0 (0) | N/A | N/A |
aNegative binomial regression model.
bNegative binomial regression model adjusted for age, sex, and Charlson comorbidity index.
cN/A: not applicable.
The analyses of within-trial costs and cost-effectiveness for all unplanned visits and hospital admissions are summarized in
Changes in health-related quality of life, within-trial costs (average cost per patient), and cost-effectiveness considering all unplanned visits and hospital admissions.
Variables | Usual care (n=30) | Integrated care (n=29) | Difference | ICERa |
Changes in the 12-item short-form survey score, mean (SD) | 16.1 (14.8) | 16.2 (14.3) | 0.1 (6.3) | N/Ab |
Unplanned visits costsc (US $) | 106.07 | 51.74 | −54.33 | N/A |
Hospital admissions costsc (US $) | 185.25 | 0 | −185.25 | N/A |
Total medical costs per patient (US $) | 291.32 | 51.74 | −239.58 | N/A |
Personalized Connected Care for Complex Chronic Patients program cost (US $) | 0 | 85.92 | 85.92 | N/A |
Total costs per patient (US $) | 291.32 | 137.66 | −153.66 | −1920.73 |
aICER: incremental cost-effectiveness ratio; incremental cost associated with one additional point gain in the 12-item short-form survey.
bN/A: not applicable.
cCosts based on the Catalan Institute of Health official pricing.
Changes in health-related quality of life, within-trial costs (average cost per patient), and cost-effectiveness considering unplanned visits and hospital admissions related to the surgical intervention.
Variables | Usual care (n=30) | Integrated care (n=29) | Difference | ICERa |
Changes in the 12-item short-form survey score, mean (SD) | 16.1 (14.8) | 16.2 (14.3) | 0.1 (6.3) | N/Ab |
Unplanned visits costsc (US $) | 64.68 | 31.05 | −33.63 | N/A |
Hospital admissions costsc (US $) | 185.25 | 0 | −185.25 | N/A |
Total medical costs per patient (US $) | 249.93 | 31.05 | −218.88 | N/A |
Personalized Connected Care for Complex Chronic Patients program cost (US $) | 0 | 85.92 | 85.92 | N/A |
Total costs per patient (US $) | 249.93 | 116.97 | −132.96 | −1661.94 |
aICER: incremental cost-effectiveness ratio; incremental cost associated with one additional point gain in the 12-item short-form survey.
bN/A: not applicable.
cCosts based on the Catalan Institute of Health official pricing.
Sensitivity analyses assuming two different cost scenarios, 150% and 200% estimated cost of the IC program, thus exploring cost-effectiveness under unplanned increases in the implementation costs, showed savings and cost-effectiveness, as shown in Tables S1 and S2 in
The prospective assessment of the implementation of an mHealth-enabled IC program for TKA and THA management showed a reduction in the number of unplanned contacts with the health system after the surgery; generated substantial savings for the health system, while not having any negative impact on QoL or clinical outcomes; and demonstrated cost-effectiveness.
The implemented IC model had several strengths that must be highlighted. First, there was an effort to involve all the stakeholders from different organizations that would be actors in a large-scale deployment of the mHealth-enabled IC program since the very early stages. This is key, as the lack of cooperation among professionals, teams, and organizations is a recurrent barrier for effective IC implementation [
Regarding this study, several strengths and limitations should be noted. Among the strengths, we note the use of a prospective study design with a comparator arm; the use of objectively measured endpoints, such as visits and admissions, in contrast to patient-reported outcomes; and cost and cost-effectiveness assessments. Concerning the limitations, the technological platform supporting the implemented IC model showed substantial improvements throughout the implementation period. This implied that patients recruited near the end of the implementation study had a richer IC experience than those recruited at the very beginning. Similarly, this had an impact on health care professionals, who had to cope with a platform under development and not fully integrated with existing EMRs. Nevertheless, participating patients and professionals showed great acceptability of the IC model and setting [
This study aimed to assess the impact of the implementation of an IC model in three domains: (1) patients’ QoL, (2) patients’ use of health services, and (3) health economics. Regarding the QoL domain, the IC model performed as good as the UC arm in the differences-in-differences analysis. This result is in line with the mixed results found in a 2017 review on the impact of IC interventions on QoL [
When specifically focusing on THA and TKA, previous studies have suggested the usefulness of the different potential components of an eHealth IC model, including telerehabilitation [
The World Health Organization has already stated the need for patient-centered IC models to satisfy the health needs of older populations with chronic diseases while keeping costs sustainable [
The implementation of a patient-centered mHealth-enabled IC model for the management of patients undergoing THA or TKA successfully empowered patients, effectively connected the different care settings involved, reduced unplanned contacts with the health system, reduced health costs, and was cost-effective. This supports the use of mHealth tools for the implementation of system-wide cross-organizational IC models.
Supplementary information.
American Society of Anesthesiologists
complex chronic patient
Personalized Connected Care for Complex Chronic Patients
electronic medical record
enhanced recovery after surgery
integrated care
incremental cost-effectiveness ratio
mobile health
osteoarthritis
quality of life
smart adaptive case management
12-item short-form survey
total hip arthroplasty
total knee arthroplasty
usual care
This work was supported by the European Union’s Horizon 2020 Research and Innovation Program (under grant agreement GA-689802). JDB acknowledges receiving financial support from the Catalan Health Department (Pla Estratègic de Recerca i Innovació en Salut 2016: SLT002/16/00364) and Instituto de Salud Carlos III (Miguel Servet 2019: CP19/00108), which is cofunded by the European Social Fund,
JDB, EV, NN, FP, FM, FB, and GT participated in the conceptualization of the project. JC, RD, MT, MM, EV, AF, MOB, and GT conducted the data collection. MM, EV, and JDB participated in the data curation. JDB conducted all statistical analyses. JC and JDB wrote the original draft of the manuscript. All authors reviewed the final manuscript. JDB, EV, FM, FB, and GT secured funding for the project. The CONNECARE-Lleida Group consists of: Maria Aguilà Balastegui, Sandra Alexandre Loxano, Laila Al-Jouja Llorente, Tomás Alonso Sancho, Enrique Aparicio Bañeres, Ana Arce Vila, Jose Maria Baron Burriel, Ramon Bascompte Claret, Albert Bigorda Sague, Emilia Blanco Ponce, Maria Boldú Franque, Àngels Bosch Roig, Carmen Bravo Santiago, Alba Capdevila Sarramona, Aida Castelló Corretge, Montse Coma Gassó, Fina Cregenzan Ortiz, Dolors Del Pozo Garcia, Mireia Falguera Vilamajó, Pere Farre Pagés, Yolanda Fauria Garcia, Anabel Fusalba Canales, Jara Gayan Ordas, Sergi Godia Lopez, Irene Gomez Companys, Jessica Gonzàlez Gutierrez, Anna Gort Oromí, Carme Jorge Tufet, Mercé Lavega Llorens, Laia Llort Samsó, Maria Rosa Lopez Cervelló, Belen Malla Clua, Josep Maria Marsol Mas, Teresita Martí Ribes, Diana Martin Capella, José Maria Martínez Barriuso, Esther Mateus Solé, Ramon Mazana Novellon, Petra Merino De los Santos, Miquel Mesas Julio, Sonia Minguet Vidal, Nuria Moles Porta, Luis Miguel Montaña Esteban, Dolors Morera Roset, Meritxell Moyà Oro, Irene Muñoz Del Campo, Francisco Nicolás Sánchez, Inés Ortiz Catalán, Mireia Ortiz Valls, Sonia Ortiz Congost, Jose Maria Palacin Peruga, Eugeni Paredes Costa, Pablo Pastor Pueyo, Ana Pérez Sainz, Antonio Plana Blanco, Anna Planas Hiraldo, Pepita Pont Aldoma, Marife Quelle Alonso, Rebeca Ramirez Molinero, Maria Àngels Revés Juanbaro, Anna Ribé Miró, Eva Ribó Caubet, Rebeca Rodriguez Corbaton, Marina Rué Florensa, Oscar Sacristán García, Irene Sanmartí Forns, Maria Cruz Sanz Martinez, Neus Sendra Bordes, Maria Cruz Urgelés Castillón, Laia Utrillo Montagut, and Montse Vidal Ballesté.
None declared.