Towards a connected health delivery framework

Background: In developing country like India it is very difficult for people of low socio-economic status to get access to healthcare and in case they seek healthcare, cost of medicines becomes major reason for out of pocket expenditure, as all the medicines are not available in PHC. Objective: To collate Ayurvedic medicine with Allopathic medicine to provide choice of treatment to patient in view of UHC. Methods: A literature review on Ayurvedic drugs (single drug and formulations) was done after prioritizing the diseases for our study based on National programs and other frequently seen diseases in Primary healthcare (PHC). Evidence was collected in two ways, first by pure Ayurvedic evidence based on Samhitas and second was based on modern techniques and then tabulated. Results: Ayurvedic drug list for Primary Health Care was formulated based on available modern as well as Classical evidence and tabulated in the form of a table. Conclusion: Ayurvedic drugs can be integrated in PHC to provide universal health care at primary level.

Download Full-text

(Preprint)

10.2196/preprints.12279 ◽

2018 ◽

Author(s):

Ram Dixit ◽

Sahiti Myneni

Keyword(s):

Cancer Survivors ◽

Mental Models ◽

Health Systems ◽

Information Needs ◽

Health Data ◽

Information Architecture ◽

Personal Health ◽

User Centered Design ◽

Card Sort ◽

Connected Health

BACKGROUND Connected Health technologies are a promising solution for chronic disease management. However, the scope of connected health systems makes it difficult to employ user-centered design in their development, and poorly designed systems can compound the challenges of information management in chronic care. The Digilego Framework addresses this problem with informatics methods that complement quantitative and qualitative methods in system design, development, and architecture. OBJECTIVE To determine the accuracy and validity of the Digilego information architecture of personal health data in meeting cancer survivors’ information needs. METHODS We conducted a card sort study with 9 cancer survivors (patients and caregivers) to analyze correspondence between the Digilego information architecture and cancer survivors’ mental models. We also analyzed participants’ card sort groups qualitatively to understand their conceptual relations. RESULTS We observed significant correlation between the Digilego information architecture and cancer survivors’ mental models of personal health data. Heuristic analysis of groups also indicated informative discordances and the need for patient-centric categories relating health tracking and social support in the information architecture. CONCLUSIONS Our pilot study shows that the Digilego Framework can capture cancer survivors’ information needs accurately; we also recognize the need for larger studies to conclusively validate Digilego information architectures. More broadly, our results highlight the importance of complementing traditional user-centered design methods and innovative informatics methods to create patient-centered connected health systems.

Download Full-text

Monitoring, Evaluation, Disease Surveillance, and Quality Improvement

10.1093/oso/9780190662455.003.0010 ◽

2017 ◽

Author(s):

Joia S. Mukherjee

Keyword(s):

Quality Improvement ◽

Medical Records ◽

Disease Surveillance ◽

Program Planning ◽

Health Data ◽

Quality Data ◽

Electronic Systems ◽

Health Delivery ◽

Use Of Data ◽

Improve Health

Quality data are necessary to make good decisions in health delivery for both individuals and populations. Data can be used to improve care and achieve equity. However, systems for health data management were historically weak in most impoverished countries. Health data are not uncommonly compiled in stacks of poorly organized paper records. Efforts to streamline and improve health information discussed in this chapter include patient-held booklets, demographic health surveys, and the use of common indicators. This chapter also focuses on the evolution of medical records, including electronic systems. The use of data for monitoring, evaluation, and quality improvement is explained. Finally, this chapter reviews the use of frameworks—such as logic models and log frames—for program planning, evaluation, and improvement.

Download Full-text

Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽

10.3390/electronics10131558 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1558

Author(s):

Muhammad Bilal Khan ◽

Mubashir Rehman ◽

Ali Mustafa ◽

Raza Ali Shah ◽

Xiaodong Yang

Keyword(s):

Psychological Health ◽

Software Defined Radio ◽

Orthogonal Frequency Division Multiplexing ◽

Additive White Gaussian Noise ◽

Vital Signs ◽

Wireless Channel ◽

Machine Learning Algorithms ◽

Breathing Patterns ◽

Connected Health ◽

Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

Download Full-text

Significance of training, monitoring and assessment of malaria workers in achieving malaria elimination goal of Malaria Elimination Demonstration Project

Malaria Journal ◽

10.1186/s12936-020-03534-9 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Harsh Rajvanshi ◽

Sekh Nisar ◽

Praveen K. Bharti ◽

Himanshu Jayswar ◽

Ashok K. Mishra ◽

...

Keyword(s):

Case Management ◽

Vector Control ◽

Malaria Elimination ◽

Disease Surveillance ◽

Training Session ◽

Central India ◽

Malaria Diagnosis ◽

Demonstration Project ◽

National Goal ◽

Health Delivery

Abstract Background The Malaria Elimination Demonstration Project (MEDP) maintained a workforce of 235 Village Malaria Workers (VMWs) and 25 Malaria Field Coordinators (MFCs) to conduct disease surveillance, case management, IEC/BCC activities, capacity building, and monitoring of vector control activities in 1233 villages of Mandla, a high malaria endemic district of Madhya Pradesh in central India. Methods The induction training was conducted for 3 days on malaria diagnosis, treatment, prevention, and ethics. All trainings were assessed using a pre and post-training assessment questionnaire, with 70% marks as qualifying threshold. The questionnaire was divided into three thematic areas viz. general knowledge related to malaria (KAP), diagnosis and treatment (DXRX), and vector control (PVC). Results In 2017, the project trained 330 candidates, followed by 243 and 247 candidates in 2018 and 2019, respectively. 94.3% candidates passed after a single training session. Almost all (95%) candidates showed improvement in knowledge after the training with 4% showing no effect and 1% showing deterioration. Progressive improvement in scores of 2017 cohort was seen along with significant improvement in performance of candidates in 2019 after the introduction of systematic monitoring and ‘shadowing’ training exercises. Conclusion The project has successfully demonstrated the value of recruitment of workers from the study area, outcome of training, and performance evaluation of field staff in malaria elimination programme. This careful strategy of recruitment and training resulted in a work-force that was capable of independently conducting surveillance, case management, vector control, and Information Education Communication/Behaviour Change Communication (IEC/BCC). The learnings of this study, including the training modules and monitoring processes, can be used to train the health delivery staff for achieving national goal for malaria elimination by 2030. Similar training and monitoring programmes could also be used for other public health delivery programmes.

Download Full-text

Methodology for Development of a Community Level Intervention Module for Physical Illness in Persons with Mental Illness (CLIPMI)

Indian Journal of Psychological Medicine ◽

10.1177/0253717620973381 ◽

2020 ◽

Vol 42 (6_suppl) ◽

pp. S94-S98

Author(s):

Ramanujam Govindan ◽

Thara Rangaswamy ◽

Sujit John ◽

Sunitha Kandasamy

Keyword(s):

Mental Health ◽

Intervention Program ◽

Intervention Group ◽

Mental Health Program ◽

Mental Illnesses ◽

Community Level ◽

Treatment As Usual ◽

Health Delivery ◽

Expected Outcome ◽

Health Delivery System

Background and Objectives: Medical illnesses seen in persons with psychiatric disorders are important but often ignored causes of increased morbidity and mortality. Hence, a community level intervention program addressing the issue is proposed. Materials and Methods: Patients with severe mental illnesses will be identified by a door-to-door survey and assessed for comorbid physical illnesses like anemia, hypertension, diabetes, and so on. They will then be randomized into two groups. The treatment as usual (TAU) group will not receive intervention from the trained community level workers, while the Intervention group will receive it. Results: The two groups will be compared for the prevalence and severity of comorbid physical illnesses. The expected outcome is compared to the TAU group, the intervention group will have a greater reduction in the morbidity due to physical illnesses and improved mental health. Conclusion: If successful, the module can be incorporated into the community level mental health delivery system of the District Mental Health Program (DMHP).

Download Full-text

Is an integrated model of school eye health delivery more cost-effective than a vertical model? An implementation research in Zanzibar

BMJ Open Ophthalmology ◽

10.1136/bmjophth-2020-000561 ◽

2021 ◽

Vol 6 (1) ◽

pp. e000561

Author(s):

Ving Fai Chan ◽

Fatma Omar ◽

Elodie Yard ◽

Eden Mashayo ◽

Damaris Mulewa ◽

...

Keyword(s):

Implementation Research ◽

Cost Savings ◽

Cost Effective ◽

Integrated Model ◽

Health Screening ◽

High Coverage ◽

Health Delivery ◽

Screening Cost ◽

Eye Health ◽

The Cost

ObjectiveTo review and compare the cost-effectiveness of the integrated model (IM) and vertical model (VM) of school eye health programme in Zanzibar.Methods and analysisThis 6-month implementation research was conducted in four districts in Zanzibar. Nine and ten schools were recruited into the IM and VM, respectively. In the VM, teachers conducted eye health screening and education only while these eye health components were added to the existing school feeding programme (IM). The number of children aged 6–13 years old screened and identified was collected monthly. A review of project account records was conducted with 19 key informants. The actual costs were calculated for each cost categories, and costs per child screened and cost per child identified were compared between the two models.ResultsScreening coverage was 96% and 90% in the IM and VM with 297 children (69.5%) from the IM and 130 children (30.5%) from VM failed eye health screening. The 6-month eye health screening cost for VM and IM was US$6 728 and US$7 355. The cost per child screened for IM and VM was US$1.23 and US$1.31, and the cost per child identified was US$24.76 and US$51.75, respectively.ConclusionBoth models achieved high coverage of eye health screening with the IM being a more cost-effective school eye health delivery screening compared with VM with great opportunities for cost savings.

Download Full-text