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Founded Year

2016

Stage

Incubator/Accelerator - IV | Alive

About Veta Health

Veta Health specializes in AI-enabled virtual care solutions within the healthcare industry. The company offers a platform that facilitates real-time and asynchronous communication between healthcare providers and patients, remote monitoring of patient vitals, personalized engagement, and care coordination tools. Veta Health primarily serves health systems, clinicians, and patients, aiming to improve patient outcomes and healthcare delivery efficiency. It was founded in 2016 and is based in Miami, Florida.

Headquarters Location

3109 Grand Avenue Suite 279

Miami, Florida, 33133,

United States

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Veta Health's Product Videos

Veta Health's Products & Differentiators

    Care Coordination Service

    Veta Health’s optional Care Coordination Service flexibly accommodates small and large-scale clinician practices.

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Expert Collections containing Veta Health

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Veta Health is included in 1 Expert Collection, including Digital Health.

D

Digital Health

11,074 items

The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.

Latest Veta Health News

Effectiveness of Telemonitoring in Reducing Hospitalization and Associated Costs for Patients With Heart Failure in Finland: Nonrandomized Pre-Post Telemonitoring Study

Feb 7, 2024

JMIR mHealth and uHealth This paper is in the following e-collection/theme issue: August 16, 2023 . Effectiveness of Telemonitoring in Reducing Hospitalization and Associated Costs for Patients With Heart Failure in Finland: Nonrandomized Pre-Post Telemonitoring Study Effectiveness of Telemonitoring in Reducing Hospitalization and Associated Costs for Patients With Heart Failure in Finland: Nonrandomized Pre-Post Telemonitoring Study Authors of this article: 2The Wellbeing Services County of Southwest Finland, Turku, Finland 3Nordic Healthcare Group, Helsinki, Finland 4Roche Diagnostics (Schweiz) AG, Zug, Switzerland 5The Wellbeing Services County of South Savo, Mikkeli, Finland 6Ceneron Advisor Oy, Helsinki, Finland 7European Health Economics Oy, Jyväskylä, Finland 8Roche Diagnostics Oy, Espoo, Finland 9Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland Corresponding Author: Abstract Background: Many patients with chronic heart failure (HF) experience a reduced health status, leading to readmission after hospitalization despite receiving conventional care. Telemonitoring approaches aim to improve the early detection of HF decompensations and prevent readmissions. However, knowledge about the impact of telemonitoring on preventing readmissions and related costs remains scarce. Objective: This study assessed the effectiveness of adding a telemonitoring solution to the standard of care (SOC) for the prevention of hospitalization and related costs in patients with HF in Finland. Methods: We performed a nonrandomized pre-post telemonitoring study to estimate health care costs and resource use during 6 months on SOC followed by 6 months on SOC with a novel telemonitoring solution. The telemonitoring solution consisted of a digital platform for patient-reported symptoms and daily weight and blood pressure measurements, automatically generated alerts triggering phone calls with secondary care nurses, and rapid response to alerts by treating physicians. Telemonitoring solution data were linked to patient register data on primary care, secondary care, and hospitalization. The patient register of the Southern Savonia Social and Health Care Authority (Essote) was used. Eligible patients had at least 1 hospital admission within the last 12 months and self-reported New York Heart Association class II-IV from the central hospital in the Southern Savonia region. Results: Out of 50 recruited patients with HF, 43 completed the study and were included in the analysis. The hospitalization-related cost decreased (49%; P=.03) from €2189 (95% CI €1384-€2994; a currency exchange rate of EUR €1=US $1.10589 is applicable) during SOC to €1114 (95% CI €425-€1803) during telemonitoring. The number of patients with at least 1 hospitalization due to HF was reduced by 70% (P=.002) from 20 (47%) out of 43patients during SOC to 6 (14%) out of 43 patients in telemonitoring. The estimated mean total health care cost per patient was €3124 (95% CI €2212-€4036) during SOC and €2104 (95% CI €1313-€2895) during telemonitoring, resulting in a 33% reduction (P=.07) in costs with telemonitoring. Conclusions: The results suggest that the telemonitoring solution can reduce hospital-related costs for patients with HF with a recent hospital admission. JMIR Mhealth Uhealth 2024;12:e51841 Introduction The prevalence of heart failure (HF) and related costs is increasing worldwide due to an aging population [ 1 ]. The estimated prevalence of HF in the adult population is 1% to 2%, increasing to 10% in older adults aged 70 years or older [ 2 , 3 ]. HF often leads to gradual or acute changes in HF symptoms (decompensation) that require repeated and prolonged hospitalization [ 4 ]. Hospital admission is a strong predictor of further hospital admission: 20% to 25% of patients with HF are rehospitalized within 1 month and approximately 50% within 5 months of discharge [ 5 ]. Decompensation requiring hospitalization is also linked to increased mortality. A European registry study following patients for 1 year after hospitalization reported mortality rates of 24% for acute HF and 6.4% for chronic HF [ 6 ]. Hospitalization accounted for around 80% of HF health care costs [ 1 ]. An early return to the hospital following discharge may be a result of incomplete inpatient treatment and poor coordination and planning of follow-up care. Even for patients with regular follow-up care, however, the signs of decompensation may not occur during cardiology visits. Patients often contact clinics when symptoms are at an advanced stage [ 7 ]. Self-monitoring of symptoms, such as increased blood pressure, weight gain, or other health status-related symptoms, is particularly important in HF management [ 4 ]. Self-monitoring requires patients to be motivated to measure symptoms associated with HF and to have access to clinical advice when symptoms appear [ 8 ]. Remote monitoring aims to improve monitoring of patients’ health status and is defined as a part of telehealth [ 9 ]. A basic level of remote monitoring involves regular and structured telephone support provided by health care professionals (HCPs) to discuss symptoms, self-monitoring measurements, lifestyle, and drug therapy. Structured telephone support can reduce HF-related hospitalization but does not seem to have an impact on the all-cause hospitalization of patients with HF [ 10 ]. Remote monitoring solutions are noninvasive stand-alone systems in which patient data on biometric measurements (such as body weight, blood pressure, and heart rate) and reported symptoms are frequently transmitted to HCPs through a secure digital system. HCPs manually review the data on digital platforms, which may also include integrated automated alerts, and necessary action is taken to optimize treatment. The effect of noninvasive telemonitoring has been compared to the standard of care (SOC) in several studies, primarily through randomized trials. Some studies found telemonitoring had a beneficial impact on reducing hospitalization [ 11 ], while others did not find any effect [ 12 , 13 ]. However, a recent meta-analysis, encompassing 91 randomized trials and observational studies, revealed that noninvasive telemonitoring reduced all-cause mortality by 16%, first hospitalization by 19%, and total HF hospitalizations by 15%. When comparing telemonitoring studies and developing optimal telemonitoring approaches, it is crucial to consider various determinants, including the telemonitoring intervention models, health care systems, and the characteristics of the population with HF in the studies [ 14 ]. There are only a few international studies that have explored the cost-effectiveness of telemonitoring compared to the SOC [ 15 - 18 ]. The objective of this nonrandomized pre-post intervention study in patients with HF with a recent (<12 month) hospitalization was to assess the effectiveness of adding a telemonitoring solution to SOC on hospitalizations and related costs in the Finnish health care system. The study compared hospitalization occurrence and related costs with SOC and following the introduction of a telemonitoring solution. Secondary outcomes included hospital admissions and total health care costs. Methods Study Design The nonrandomized pre-post intervention study was performed in Southern Savonia, Finland. During the 12-month study period, patients were treated with SOC for the first 6 months and then with a telemonitoring solution in addition to SOC for the next 6 months. The primary outcome was hospitalization-related costs during 6 months with SOC versus telemonitoring. Secondary outcomes included the number of patients with at least 1 hospital admission due to HF or a cardiovascular cause other than HF emergency care visits and primary care or cardiology (secondary care) calls and visits. Health care costs for secondary outcomes included the total health care costs of primary care, secondary care (for cardiology), emergency visits, and phone calls. The study was designed to demonstrate the effectiveness of remote monitoring within the Finnish health care system. The costs of the telemonitoring service itself were not analyzed. Health care resource use was collected for each patient during SOC and telemonitoring from the patient register of the Southern Savonia Social and Health Care Authority (Essote). The data was pseudonymized by the register holder. The Health Care Authority is responsible for all social and health care services for the population of approximately 100,000 inhabitants in Southern Savonia, Finland. Study Patients Patients were recruited from Mikkeli Central Hospital in Finland’s Southern Savonia region. Patients with an HF diagnosis confirmed by a cardiologist, at least 1 hospital admission in the 12 months preceding study initiation, and self-reported New York Heart Association (NYHA) class II-IV were eligible for the study ( Figure 1 ). The inclusion criteria also stated that patients must be able to manage the telemonitoring devices and digital platform used in the study. Palliative care was an exclusion criterion. ‎ Study Procedures The SOC, during the first 6 months, included regular cardiology appointments and laboratory tests planned by a cardiologist for each patient with HF according to local care guidelines for HF treatment. Nurses followed up with patients through phone calls, depending on the state of HF. After inpatient stays, the cardiologist or internist at the hospital made an individual plan for the follow-up of patients posthospitalization. During the follow-up period, patients measured their weight and blood pressure at home, and nurses followed up with patients through phone calls to discuss their health status and measurement results. Telemonitoring was added on top of SOC during the next 6 months and consisted of a digital platform, home measurement devices, and nurses monitoring patients through the digital platform. The digital platform used Veta Health’s remote patient monitoring platform (Veta Health Inc), customized for the study. Patients used their smartphones, handheld devices, or personal computers to access the digital platform. Patients measured their weight daily with a digital scale (Omron Corporation) and their blood pressure with a digital blood pressure measuring device (Omron M7000 Intelli IT) and transferred the measurements into the digital platform ( Figure 2 ). The digital platform also included symptom-related questions. ‎ Figure 2. A schematic presentation of the remote patient management model. KCCQ-12: Kansas City Cardiomyopathy Questionnaire-12; PHQ2: Patient Health Questionnaire-2; PROMIS: Patient Reported Outcomes Measurement Information System. The digital platform automatically compared patients’ body weight and HF symptom answers against preset thresholds and generated semiurgent or urgent alerts predicting HF worsening (Table S1 in Multimedia Appendix 1 ). Depending on the alert type, the digital platform either advised the patient to contact a nurse or a nurse to contact the patient to validate the health status. Nurses had access to alerts on working days. If needed, nurses referred a patient to a cardiologist to optimize HF care or medication. The treating cardiologist reacted to the nurses’ referrals within 24 hours. For urgent alerts, the digital platform advised patients to go to emergency care. Nurses also provided technical support for patients as required. The digital platform collected blood pressure data and laboratory results from regular health care visits, not for the alert algorithm but to allow nurses to evaluate the patient’s health status. Health Care Resource Use and Costs Patients’ health care costs and resource use were estimated from the Essote patient register and consisted of public primary care, secondary care (cardiology unit), emergency visits, hospitalizations (cardiology and internal medicine; primary care), and phone calls for primary and secondary care (cardiology). A unique personal identification number for each resident in Finland connected the digital platform data and patient register data. The International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code registered for each health care event was used to separate hospitalizations for HF (ICD-10 code I50) from hospitalizations for a cardiovascular cause other than HF (ICD-10 codes I10, I25, I42, I46, I48, I49, and I70). All-cause hospitalizations included hospitalizations with any diagnosis. The costs of health care use were calculated using Essote diagnosis-related group prices. Statistical Analysis The study analysis included only patients who completed the study. Patients who died during the study or discontinued the study were excluded. Patient demographics and NYHA class were summarized as n (%) of patients per category or median (IQR). During the SOC and telemonitoring periods, n (%) of patients with at least 1 hospitalization and the mean number of inpatient days per patient (95% CI) were reported. The mean number (95% CI) of visits per patient (primary, secondary, and emergency) and the mean number of calls (primary and secondary care) per patient were also reported for each period. Mean health care costs per patient were reported for each period. The normal distribution of each variable was assessed through visual inspection and the Shapiro-Wilk test. For data found to be nonnormally distributed, differences between SOC and telemonitoring periods were tested using the Wilcoxon signed rank test, and a value of P<.05 was considered statistically significant. The Pearson chi-square test with Yates correction was used for testing the difference between SOC and telemonitoring periods (a binary variable) in the number of patients with at least 1 hospitalization. Ethical Considerations The ethics committee of the Northern Savonia Hospital District approved the study protocol (1401/2020). The study followed good clinical practice following the Declaration of Helsinki and the laws and regulations applicable in Finland. Patients gave written consent upon recruitment to the study. Participation in the study was voluntary and no financial compensation was awarded for participation. Results Study Population and Patient Characteristics Between December 15, 2020, and March 24, 2021, a total of 50 patients with HF were recruited from the Mikkeli Central Hospital. A total of 7 patients did not complete the study due to their deaths or withdrawals from it. All 43 (86%) patients who completed the 12-month study period were included in the analysis. During the telemonitoring period, 20% (9/43) of the daily weight and blood pressure measurements were missing. The median age of patients was 73 (IQR 66-80) years, 74% (37/50) were male, and 60% (30/50) of patients had NYHA classes III-IV ( Table 1 ). Table 1. Patient demographics and disease characteristics (n=50). Characteristic Health Care Resource Use Significantly fewer patients (6 patients in telemonitoring vs 20 patients in SOC; P=.002) had an HF hospitalization during the telemonitoring versus SOC period. The number of inpatient days per patient due to HF decreased by 48% during the telemonitoring period (mean 1.2, 95% CI 0.1-2.3 days vs 2.3, 95% CI 1-3.6 days with SOC; P=.17). The number of emergency care visits decreased significantly during the telemonitoring period by 44% (mean 0.7, 95% CI 0.4-1 vs mean 1.3, 95% CI 0.9-1.7 with SOC; P=.006). Patients with HF made significantly more phone calls to secondary care during the telemonitoring period (mean 8.3, 95% CI 6.6-10 vs mean 2, 95% CI 1.3-2.7 with SOC; 318% increase; P.001) and had significantly more primary care visits (mean 4, 95% CI 2.2-5.8 vs mean 2.8, 95% CI 1.7-3.9; 44% increase; P=.02; Table 2 ). Table 2. Use of health care per patient in standard of care (SOC) or telemonitoring solution for a 6-month period. Statistics were calculated with the Wilcoxon signed rank test or the Pearson chi-square test with Yates correction for the binary variables. Variable Health Care Costs Mean hospitalization costs per patient decreased significantly by 49% during the telemonitoring period (mean €1114 vs €2189 with SOC; P=.03; a currency exchange rate of EUR €1=US $1.10589 is applicable), while total health care costs decreased by 33% (mean €2104 vs €3124 with SOC; P=.07; Table 3 ). The cost of emergency care visits was also significantly lower in the telemonitoring period (mean €209 vs €347 with SOC; 40% decrease; P=.009), and mean costs per patient for phone calls to secondary care increased significantly (mean €268 vs €114 with SOC; 134% increase; P.001) in the telemonitoring period ( Table 3 ). Table 3. Estimated mean direct health care cost per patient in standard of care (SOC) and in telemonitoring solution, respectively, during a 6-month period (2021). A currency exchange rate of EUR €1=US $1.10589 is applicable. Statistics were calculated with the Wilcoxon signed rank test. Cost category

Veta Health Frequently Asked Questions (FAQ)

  • When was Veta Health founded?

    Veta Health was founded in 2016.

  • Where is Veta Health's headquarters?

    Veta Health's headquarters is located at 3109 Grand Avenue, Miami.

  • What is Veta Health's latest funding round?

    Veta Health's latest funding round is Incubator/Accelerator - IV.

  • Who are the investors of Veta Health?

    Investors of Veta Health include Healthtech Accelerator, MassChallenge, Plug and Play, Startup Creasphere and JLabs.

  • Who are Veta Health's competitors?

    Competitors of Veta Health include Reimagine Care and 4 more.

  • What products does Veta Health offer?

    Veta Health's products include Care Coordination Service and 4 more.

  • Who are Veta Health's customers?

    Customers of Veta Health include St. Joseph Mercy Oakland, Cardiology and Vascular Associates, PC, Roche Diagnostics Netherlands & Netherlands Heart Network and Roche Diagnostics Finland & ESSOTE.

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