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Visiting Nurses

Visiting Nurses: Highly skilled healthcare professionals who provide comprehensive in-home care to patients, empowering them to achieve optimal health outcomes.
These nurses leverage cutting-edge technology and data-driven insights to identify the most effective treatment protocols, streamlining the care process and enhancing patient experiences.
With their expertise and dedication, Visiting Nurses play a vital role in promoting wellness and independence within the community.
Efforlessly locate and compare relevant protocols from literature, preprints, and patents to deliver personalized, evidende-based care tailored to each patient's unique needs.

Most cited protocols related to «Visiting Nurses»

National Survey of Health and Development

The NSHD is a representative sample of 5362 males and females who were born in England, Scotland and Wales in one week in March 1946 [8 (link), 13 (link), 24 (link)]. At the 24th follow-up, the target sample was 2816 study members still living in mainland Britain; this is the maximum sample used in the analyses. Of the remaining 2546 (47%) study members: 957 (18%) had already died, 620 (12%) had previously withdrawn permanently, 574 (11%) lived abroad, and 395 (7%) had remained untraceable for more than 5 years.
The postal questionnaire was sent out when study members were age 68; up to two reminder letters were sent to those who did not return a completed questionnaire. The home visit by a research nurse including blood sample collection took place at age 69 for the majority (97%), and at 70 years for the remainder. A short questionnaire, covering questions the research nurse asked at the home visit was posted to study members who did not wish to have a nurse visit. For this data collection, we obtained ethical approval from the NRES Queen Square REC (14/LO/1073) and Scotland A REC (14/SS/1009). This also included a protocol for collecting data on behalf of those who no longer had capacity to give consent (supplementary information).

Kuh D., Wong A., Shah I., Moore A., Popham M., Curran P., Davis D., Sharma N., Richards M., Stafford M., Hardy R, & Cooper R. (2016). The MRC National Survey of Health and Development reaches age 70: maintaining participation at older ages in a birth cohort study. European Journal of Epidemiology, 31(11), 1135-1147.

Publication 2016

Childbirth Females Males Nurses Specimen Collections, Blood Visiting Nurses

Comprehensive Healthcare Database in Stockholm

Stockholm County has 2.1 million inhabitants, representing more than one-fifth of Sweden’s entire population. This area of Sweden includes the capital city of Stockholm and several other cities and towns, as well as large rural areas and a sparsely-populated archipelago. The Stockholm County Council is responsible for financing primary and secondary health care, mainly through taxes. Besides illegal immigrants, the general health insurance covers all residents. The majority of services are provided by County-owned facilities. However, during the last decade, more than 50% of primary care and approximately 8% of acute hospital care services have been outsourced to private providers, either through tender processes or managed patient choice [9 (link)]. Private providers are in contractual agreements with the County and are legally obliged to record diagnoses and file reports to the authorities just like public providers [10 (link)]. With the exception of very few private clinics that operate without subsidies in Stockholm, all consultations and diagnoses are recorded and stored in the so-called VAL, a central database. Besides consultations and diagnoses, VAL compiles and stores data on healthcare utilization and socio-demographics. The database has been used for healthcare planning, practice remuneration and quality assessment since the beginning of the 1980s, and its content, registration routines, and supporting software have improved over the years. It has previously been used as a source of information in a number of scientific studies, e.g. studies of hip fractures and its co-morbidities [11 (link),12 (link)], and Parkinson’s disease [13 ]. As an indication for its accuracy and validity, VAL is used by the Council for updating the National Patient Register kept by the Swedish National Board of Health and Welfare (NBHW) as well as the annual benchmarking reports of the NBWH and the Swedish Association of Local Authorities and Regions [4 (link)].
VAL has more than 99% coverage of hospital care. More specifically, for each hospital stay the VAL database contains a record of the provider unit, an encrypted patient identification number, age and sex, the type and length of the stay, up to ten diagnoses given, and ten interventions (primarily surgical procedures, transfusions, anaesthetic procedures). Since 1997, diagnoses have been coded according to WHO’s International Classification of Diseases, 10^th edition (ICD-10) and procedures classified according to the Nordic Classification of Surgical Procedures (NCSP).
Reporting utilization in specialized ambulatory care, whether in hospital clinics or other locations, is also mandatory since the late 1990s. This includes reporting diagnoses (up to eight) for consultations by a physician, date, type of visit, and certain procedures performed. Also, nurse visits in homecare, visits to occupational or physical therapists as well as most other healthcare professionals are registered. VAL has more than 90% coverage of utilization in specialized ambulatory care.
Storing data on primary care diagnoses in VAL has a shorter history. In 2003, a project to extract information on diagnoses (up to 15) from the electronic medical records, when available, was launched. It has been estimated that approximately 85% of all diagnoses in primary care are stored in VAL.
All data extracted from VAL were anonymised in the current study.

Carlsson A.C., Wändell P., Ösby U., Zarrinkoub R., Wettermark B, & Ljunggren G. (2013). High prevalence of diagnosis of diabetes, depression, anxiety, hypertension, asthma and COPD in the total population of Stockholm, Sweden – a challenge for public health. BMC Public Health, 13, 670.

Publication 2013

Anesthetics Blood Transfusion Care, Ambulatory Diagnosis Health Care Professionals Health Insurance Hip Fractures Operative Surgical Procedures Patients Physical Therapist Physicians Primary Health Care Secondary Care Undocumented Immigrants Visiting Nurses

Continuity of Care and Healthcare Utilization

The present study was based on retrospective data of members of Clalit for 2009. The Clalit database includes 4,000,000 enrollees. Sampling the entire database is feasible using the last digit of the ID number and/or the two digits before the last one. As It is technically non-feasible to run queries on a population of that size, we combined these two methods to yield about 4,000 enrollees, of whom 1,713 met our inclusion criteria - patients aged 19 years or older, who visited their primary care physician at least three times in 2009, from a population of 2,649,870 enrollees aged 19 years or older.
The main goal of the study was to identify associations of continuity of care indices with healthcare utilization pattern by a logistic regression model. We estimated that up to 20 variables are likely to be included. Since the rule of thumb is to include at least 15 observations per parameter, at least 300 patients would have to be included in the analysis to yield significant results [37 ]. Therefore, the sample size available for analysis (1,713 patients) was satisfactory. The cutoff of 3 visits was necessary because continuity of care is always perfect for patients with one visit, and even among patients with two visits, values of indices could shift from 0 to 1 with minute changes in the patterns of visits. Patients treated at a group-practice clinic were excluded, because in such clinics more than one physician is the regular source of care for the patient and the individual physician that participated in the clinical encounter could not be identified. These patients comprised 8% of Clalit's enrollees in 2009 (7% of clinics). No other exclusions were made. Pregnant women were included in the analysis.
Variables used in this study were derived from Clalit's computerized databases. Clalit maintains a comprehensive database that includes demographic information, utilization of primary and consultative medicine services, laboratory tests and imaging, ED visits, hospitalizations, chronic diagnoses, medications, and primary medicine quality measures. The accuracy of Clalit's database for chronic diagnoses has been previously reported to be high [38 (link)]. Almost all Clalit members have a single regular physician. For each visit, data include date of visit and type of visit (ordinary visit, house call, telephone call, visit without the patient's presence [visits for renewing prescriptions or issuing medical documents for the patient], visits for administrative reasons, and unknown/undefined type of visit.). The current study included ordinary visits and house calls only. Visits to the nurses' room only were not included in the present analysis.
The following four continuity of care measures were computed for each patient, based on formulas described in the literature [3 ,9 (link),20 ,25 ] (see Appendix 1 for formulas and illustrative examples):

Dreiher J., Comaneshter D.S., Rosenbluth Y., Battat E., Bitterman H, & Cohen A.D. (2012). The association between continuity of care in the community and health outcomes: a population-based study. Israel Journal of Health Policy Research, 1, 21.

Publication 2012

Continuity of Patient Care Diagnosis Diet, Formula Fingers Hospitalization Patients Pharmaceutical Preparations Physicians Pregnant Women Prescriptions Primary Care Physicians Visit, Home Visiting Nurses

Decision-Making Assessment in Older Adults

A 12-item version of a previously established performance-based measure was used to examine decision making(2 (link)-3 (link), 11 (link)-14 (link)). This measure was specifically designed to evaluate decision making in older adults and uses theoretically-based and conceptually well-considered items to assess aspects of decision making such as comprehension and integration of information using materials that closely resemble those used in real world decision settings commonly faced by older persons (2 (link)-3 (link)). Respondents are asked questions of varying difficulty levels (simple and complex). Simple questions primarily measure decisions that reflect understanding of the information presented. The complex problems parallel the simple problems but involve many more options. For example, one of the simple presents information on three mutual funds, including the gross annual return, account management fee, minimum investment, and years of activity, and asks respondents to select the fund with the smallest account management fee. Subsequently, a complex problem presents similar information about seven mutual funds and asks respondents to select the most appropriate fund given pre-specified preferences (e.g., Pamela wants a management fee of less than X%, a gross annual rate of return of X%, and a minimum investment of X; which fund should she choose?). The total score is the sum of number of items answered correctly (range=0-12), with higher scores indicating better decision making. In previous research, this measure has been shown to have adequate psychometric properties including internal consistency, inter-rater reliability and short-term temporal stability. Further, an expanded version of the test has been shown to be related to behaviors and abilities known to be related to decision making in old age, including measures of cognitive ability, numeracy, attitudinal and psychosocial measures (e.g., motivation) and health behaviors (e.g., number of medications taken, number of doctor/nurse visits) (2 (link)-3 (link)). Finally, in our cohort, performance on the test is related to real world decisions such as susceptibility to scams (p<0.001) and likelihood of having been a victim of financial fraud (p<0.01), in addition to other indices that should be related to decision making in old age (e.g., age, education, income, conscientiousness, and functional status, all p’s<0.001).

Boyle P.A., Wilson R.S., Yu L., Buchman A.S, & Bennett D.A. (2013). Poor Decision Making is Associated with an Increased Risk of Mortality Among Community-Dwelling Older Persons without Dementia. Neuroepidemiology, 40(4), 247-252.

Publication 2013

Aged Cognition Motivation Pharmaceutical Preparations Physicians Psychometrics Susceptibility, Disease Visiting Nurses

Comprehensive Study of H1N1pdm Determinants

The main objective of this study was to identify individual and collective determinants of H1N1pdm infection; therefore we tried to collect comprehensive data about subjects and their environment, in addition to biological samples. Several household visits are carried on by nurses for this purpose (see Figure 3 for details).
· Inclusion visits During the inclusion visit, nurses collected from all subjects detailed data regarding medical history, vaccination and preventive measures against influenza, smoking habits, socioeconomic status, risk perception and beliefs, frequency and characteristics of meetings with other people and housing (personal room, house or apartment). As the households’ addresses were geocoded, we were able to get additional information from public databases regarding the immediate surrounding environment of households. An overview of data collected from questionnaires at entry in the cohort is shown in Figure 4. Blood samples were collected and centralized for serological analyses. For subjects over 10 years, a heparinated tube was also collected to study cellular immunity, as well as a blood sample dedicated to transcript analyses.
· Systematic yearly visits After the inclusion visit, systematic follow-up visits are carried on between influenza seasons. During a systematic visit, a nurse collects or updates individual and environmental data on questionnaires, completes previously missing data, and obtains blood samples from all members of the household. Two waves of systematic follow-up visits have already occurred (summer-fall 2010 and 2011). A third wave is expected by the end of the second year of follow-up (summer 2012).
· Influenza-like illness (ILI) visits During the influenza season (as defined by the French surveillance network [21 ]), we use an active surveillance system order to detect ILIs: all households are called by an interactive voice response system (IVRS) weekly and are asked if any subject has symptoms of ILI (fever ≥ 37.8°C associated with cough or sore throat, as defined by the CDC [22 ]). A free phone number is given to subjects to report symptoms spontaneously between two weekly calls. In case of reported ILI, symptoms are validated by the study team and then three “ILI visits” are organized: nurses visit the household within 48 h after the onset of symptoms, then 3–6 days and 8–12 days after the onset.
· During these visits, a detailed questionnaire collects data about the circumstances of possible exposure to influenza viruses and the chronology of symptoms (if any) in all subjects. Nasal swabs are collected from all subjects. A stool sample and a throat swab are also collected from subjects with ILI, as well as a blood sample from those over 10 years of age. Moreover, a self-swab procedure is previously sent to the households in order to collect virological samples when a visit by a nurse within the first 48 h is not possible. Nasal swabs are used to identify various respiratory viruses by PCR and biochips allowing for multiple diagnosis tests.
· This series of three visits can occur several times in the same household during an influenza season. There were 23 ILI alerts during the 2009–2010 season (as households were still being included) and 143 during the 2010-2011 season, all of which triggered up to three ILI visits.
· Vaccination visits In order to update serological information, a blood sample was collected from subjects who had an influenza vaccination, between 2 and 4 weeks following this vaccination. There was one vaccination visit following the inclusion visits; 29 vaccination visits were conducted following the first wave of follow-up visits and 69 following the second wave.

Lapidus N., de Lamballerie X., Salez N., Setbon M., Ferrari P., Delabre R.M., Gougeon M.L., Vely F., Leruez-Ville M., Andreoletti L., Cauchemez S., Boëlle P.Y., Vivier E., Abel L., Schwarzinger M., Legeas M., Le Cann P., Flahault A, & Carrat F. (2012). Integrative study of pandemic A/H1N1 influenza infections: design and methods of the CoPanFlu-France cohort. BMC Public Health, 12, 417.

Publication 2012

Biopharmaceuticals BLOOD Cellular Immunity Cough Diagnosis Feces Fever Households Infection Nose Nurses Orthomyxoviridae Pharynx Respiratory Rate Sore Throat Vaccination Virus Virus Vaccine, Influenza Visiting Nurses

Most recents protocols related to «Visiting Nurses»

Multicomponent Digital Health Intervention

Following a booking for a visit to the public health center, the participants underwent a multicomponent intervention which included a consent form, pre-evaluation, 6 months of non-face-to-face health counseling, and health management information for using ICT devices. At the end of the 6-month service, the same items were subject to a post-evaluation. All the participants received non-face-to-face health counseling at least once a month. The healthcare missions consisted of the following: eating 3 meals per day, walking 5,000 steps or 30 min per day, taking prescribed medication on time, going outside at least once a day, measuring blood pressure once a day if participants had hypertension, measuring glucose level regularly if participants had hyperglycemia and drinking 8 cups of water per day (see Supplementary Figure S1). The participants connected their health data (step count, blood pressure, blood glucose, healthcare mission) to the smartphone app through wearable devices in real-time. This information was remotely monitored by visiting nurses, exercise experts, nutritionists, and other experts from the health center. Non-face-to-face consultations were conducted more than once based on this information. Health education materials were also provided in a non-face-to-face manner, and pictures or video links related to healthcare were sent to the participants’ mobile phones at least once a month. Using the app's push notifications, we sent a text message encouraging the participants to perform a healthcare mission at least once a week. The home care nurses monitored blood pressure, blood glucose levels, and step count levels at least once a week and provided consultations if there were any abnormalities. Table 1 presents the functions of smart speakers, smartphone apps, and wearable devices provided for each group.

, & Kim D. (2023). Can healthcare apps and smart speakers improve the health behavior and depression of older adults? A quasi-experimental study. Frontiers in Digital Health, 5, 1117280.

Publication 2023

Blood Glucose Blood Pressure Congenital Abnormality Face Glucose High Blood Pressures Hyperglycemia Medical Devices Nurses Nutritionist Pharmaceutical Preparations Visiting Nurses

IoT-Assisted Aging-in-Place Study

Our participant sample consisted of older adults aged 65 or older registered for the home care services provided by a public health center in Seoul. The criteria for the selection of study participants were the ability to maintain cognitive function to use IoT devices and to understand the survey or follow the instructions of the visiting nurse. Chronic diseases such as hypertension, diabetes, cancer, hyperlipidemia, cerebrovascular disease, and cardiovascular disease may be present in the participants. The exclusion criteria for participation in the study were those diagnosed with dementia or significantly reduced cognitive function who were unable to complete questionnaires and follow the visiting nurse's instructions. People who had taken drugs or been diagnosed by a doctor for alcoholism, depression, schizophrenia, or any other type of psychosis were also excluded. If it was determined that it was impossible for a participant to continue participating in the study due to a deterioration of health during the study or if they passed away, they were excluded. Further, if voluntary participation was difficult to guarantee, or if participants wanted to withdraw their participation, these individuals were removed from the study.

, & Kim D. (2023). Can healthcare apps and smart speakers improve the health behavior and depression of older adults? A quasi-experimental study. Frontiers in Digital Health, 5, 1117280.

Publication 2023

Alcoholic Intoxication, Chronic Cardiovascular Diseases Cerebrovascular Disorders Cognition Dementia Diabetes Mellitus Disease, Chronic High Blood Pressures Hyperlipidemia Malignant Neoplasms Medical Devices Pharmaceutical Preparations Physicians Psychotic Disorders Schizophrenia Visiting Nurses

Longitudinal Aging Study of English Adults

The English Longitudinal Study of Ageing (ELSA) is an ongoing panel study that is nationally representative of adults aged 50 + and their partners, who reside in private households in England. The Study began in 2002 (Wave 1), with responses from 12,099 individuals from 7,934 households. Every two years, sample participants are interviewed on various dimensions of health, social, cognitive, and economic wellbeing; in addition, every four years, nurse visits are conducted for the collection of biological samples and anthropometric measurements. The most recent sweep of the study is Wave 9, with data collection spanning June 2018 and July 2019. The sample has also been refreshed at Waves 3, 4, 6, 7, and 9, to ensure the sample remains nationally representative^{45 (link)}.
We also use Wave 1 of the ELSA COVID-19 Substudy, administered between June 3 and July 26, 2020. The survey was issued to 9,392 eligible members in 6,173 households, with 7,040 interviews completed (75% response rate). The survey was administered online (83%) or by telephone interview for those who unable to respond online (17%). From the 7,040 completed interviews in the COVID-19 Substudy Wave 1, 6,840 interviews provided non-missing Internet use data (after applying cross-sectional weights provided by the ELSA team)⁴¹.
The English Longitudinal Study of Ageing received ethical approval from the South Central-Berkshire Research Ethics Committee (21/SC/0030, 22nd March 2021), and the COVID-19 Substudy was approved by the University College London Research Ethics Committee. Informed consent was obtained from all participants, and all analyses in this study is performed in accordance with Committee guidelines.

Kung C.S, & Steptoe A. (2023). Changes in Internet use patterns among older adults in England from before to after the outbreak of the COVID-19 pandemic. Scientific Reports, 13, 3932.

Publication 2023

Adult Cognition COVID 19 Ethics Committees, Research Households Specimen Collection Visiting Nurses

Comprehensive Evaluation of Cognitive Decline

All 509 surviving ULSAM participants were invited to ULSAM-88 at the Geriatric department, Uppsala University hospital between September 2008 and December 2009; and 308 participated at the hospital or had a home visit by a research nurse (‘subgroup participating in tests’). For individuals who did not participate in ULSAM-88 (201/509), phone interviews were conducted, as well as questionnaires and a review of medical charts from January 2010 to March 2010. The diagnostic workup for dementia was made according to standard clinical procedures at either the Memory Clinic (mandatory for a diagnosis of Lewy body dementia and frontotemporal dementia) or in primary care, and cases were identified by reviews of the medical records. Caregivers’ descriptions of cognitive problems, impact on IADL, MMSE and Clock test were always included. The cognitive tests administered to participants in ULSAM-88 were on separate occasions, and were not included in these diagnostic procedures. Dementia was diagnosed by two experienced geriatricians (LK and KF) who independently examined all records and cognitive data available at the end of ULSAM-88/March 2010. Established diagnostic criteria and neuroradiology were used to classify cases as Alzheimer’s disease [17 (link)], vascular dementia [18 (link)], mixed Alzheimer’s and vascular dementia, Lewy body dementia/Parkinson dementia [19 (link), 20 (link)], or frontotemporal dementia [21 (link)]. A diagnosis of unspecified dementia was set in cases with insufficient information. A third geriatrician was consulted in case of discordance, and a majority decision was made.A questionnaire was administered to acquire data on living conditions, everyday physical activity, and PADL-function. For test participants, research nurses collected data on gait speed [22 (link)], ability to stand without support for 30 seconds [23 (link)], and Timed Up-and-Go Test [24 (link)]. Further, they administered the MMSE [12 (link)], enhanced cued recall (16 items) and verbal fluency (animals) from the 7 Minute Screen [13 (link)], the Geriatric Depression Scale-20 [25 (link)]; and a 0–100% visual analogue quality of Life scale [26 (link)].

Lindvall E., Franzon K., Lundström E, & Kilander L. (2023). The impact of stroke on the ability to live an independent life at old age: a community-based cohort study of Swedish men. BMC Geriatrics, 23, 126.

Publication 2023

Alzheimer's Disease Animals Cognition Cognitive Testing Dementia Dementia, Vascular Diagnosis Geriatricians Lewy Body Disease Memory Mental Recall Mini Mental State Examination Neoplasm Metastasis Nurses Physical Examination Pick Disease of the Brain Presenile Dementia Primary Health Care Tests, Diagnostic Visiting Nurses Visual Analog Pain Scale

Comprehensive Care Plan for Chronic Pain

After the inclusion interview, information was collected on actions to be implemented by RESRIP both at the paramedical level (need for orthopedic devices, social assistance, occupational therapyhypnosis or related approaches for chronic pain, home care nurse, physical therapy, general practitioner, pediatrician, psychological support and diet monitoring) and school level (school planning, test accommodations, arrangement for school sports activity).

El Haddad R., El Asmar K., Hascoët C., Rossi-Semerano L, & Dusser P. (2023). Well-being in chronic pediatric inflammatory rheumatic diseases: the experience of a French healthcare network. Orphanet Journal of Rare Diseases, 18, 46.

Publication 2023

Chronic Pain Diet Ocular Accommodation Orthopedic Equipment Pediatricians Therapy, Physical Visiting Nurses

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More about "Visiting Nurses"

Visiting Nurses, also known as Home Health Nurses or Community Nurses, are highly skilled healthcare professionals who provide comprehensive in-home care to patients.
These nurses leverage cutting-edge technology, such as electronic scales (e.g., MDA 180), Roche Elecsys vitamin D3 assays, and IPro2 CGM systems, as well as data-driven insights from statistical software like SPSS Statistics version 22, Stata/MP 14.0, and Stata/SE 15.1, to identify the most effective treatment protocols and streamline the care process.
By effortlessly locating and comparing relevant protocols from literature, preprints, and patents using tools like PubCompare.ai's AI-driven protocol optimization, Visiting Nurses are able to deliver personalized, evidence-based care tailored to each patient's unique needs, empowering them to achieve optimal health outcomes.
With their expertise and dedication, Visiting Nurses play a vital role in promoting wellness and independence within the community, utilizing techniques such as the QIAamp DNA Mini Kit and the QuickPlex SQ 120 system to enhance patient experiences and improve overall healthcare delivery.
Visiting Nurses are true healthcare heroes, leveraging cutting-edge technology and data-driven insights to provide comprehensive, personalized care that makes a real difference in the lives of their patients.