SPECIAL ISSUE
Suchitra Mankar* and Nikhilesh Paradkar
Department of Community Medicine, Doorstep Health Services, Pune, India
Background: Scarcity of primary care is felt most in underserved communities. Telemedicine (TM)-enabled clinics bridge the gap in such scenarios. There was a need to understand how the same TM model would work in different settings.
Aim: The aim was to study outcomes in three identical TM-enabled clinics in different geographies so as to understand how to scale up clinics in future.
Setting: Three totally different sites were chosen: a rural village with low socioeconomic status, a rurban (rural-urban) prosperous village, and an urban slum. The clinics planned was identical. The process of establishment, training, recruitment and treatment guidelines were the same. Any deviation was noted.
Methodology: Data were gathered through public health survey, interactions with villagers and local leaders, medical examination of individuals, feedback from patients, and household survey to understand the socioeconomic status of the community.
Main outcome measures: The article attempted to study how different social, cultural, and economic settings affected the outcome of identical TM clinics.
Results: TM, though accepted in different settings, was not sufficient to meet the healthcare needs of the community. These needs were related to the social and economic characteristics. Public health initiatives along with TM were most beneficial. In the underserved areas, infrastructure posed challenges to implementing TM, and ‘Last Mile Care Delivery’ was essential to create the full impact of TM.
Conclusion: TM-enabled clinics along with last mile care delivery are the key to improve healthcare in underserved communities. Further research into customized TM models for different geographies would help in providing the best care.
Limitations of the study: The study period was 4 months. The study was in one state of India, so the applicability of the findings to other states/countries may vary.
Keywords: telemedicine; rural health; underserved communities; social issues; primary healthcare; last mile care delivery
Citation: Telehealth and Medicine Today 2021, 6: 253 - http://dx.doi.org/10.30953/tmt.v6.253
Copyright: © 2021 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Published: 23 April 2021
Competing interests and funding: There is no conflict of interest and Bharat Forge India supported the study.
*Correspondence: Suchitra Mankar. Email: suchitramankar@gmail.com
Primary care has the potential to be the supporter of Information Technology (IT)-driven services such as telehealth, telecare, and on-line consultations. This requires the parallel development of new models of service delivery and experience from rural and remote areas (1). In India, primary care is the most needed yet the least available.
While true for the entire country, the problem is especially felt in underserved communities. A strong primary care and public health network has the potential to improve this situation and lead to a better health equity. The Government of India is aware of this and has launched a slew of measures to address the issues of rural healthcare. The National Health Policy 2017 recommended strengthening the delivery of TM-enabled clinics through the establishment of ‘Health and Wellness Centers’ as the platform to deliver comprehensive primary healthcare and called for a commitment of two-thirds of the health budget to primary healthcare (2). On perusing the plans, one concludes that they are good in concept and ambitious in their scope. However, the ground reality is that pace of reform is slow and patchy and not keeping pace with the vision. The lack of primary healthcare is felt most in underserved communities—villages, urban slums, and similar settings. TM-enabled clinics seem to be the panacea to this need. They help to bridge the gap by providing access, improving reach and responsiveness, and helping in record keeping, analysis, and evaluation. Many private players are keen to explore this space but need to understand the best business model for scale and sustainability. Indeed, the government too is keen to explore public–private partnerships. To quote an article in The British Medical Journal: ‘The evidence demonstrates that TM as a technology is worth it, although return on investment varies with the care pathway and, most significantly, in the ability of the team to implement effectively…Local implementers must concentrate on the science of implementation – the things to do and things to avoid, learning from others, both successful and unsuccessful’ (3). Keeping the above in mind, the questions that we wanted answers to were as follows: How would our TM-enabled clinic perform in different settings? Would the outcomes be the same given the same inputs, but different environmental and social characteristics? What were the perceptions of the people as regards TM-enabled consultations, type of care provided, and willingness to pay for care? What infrastructure was available or not? We felt that this information was important before scaling up our model. It was with this in mind that this study was commissioned.
The aim was to study the outcomes in three identical TM-enabled clinics in different geographies so as to understand how to scale up clinics in future.
The study period was February 01, 2019 to May 31, 2019. The study was terminated at this point as it was felt that adequate information had been gathered. Three sites were chosen to ensure them to be as different as possible (within the constraints of logistics). All sites were within 150 km of Pune. The three sites, Ruhi (rurban well-developed prosperous village), Rulow (rural poorly developed village), and Urslum (an urban slum), were selected for the study (Names changed to protect identity). The clinics set up at the sites (Spokes) were identical in terms of processes but differed in the infrastructure provided based on local conditions.
The sites were visited and an appropriate place was finalized. Network access dictated the site selection. As villagers walk, the site at Rulow was quite far; however, this was the only place with network. Initial manpower requirement was envisaged as one nurse (GNM at least) and one attendant at each clinic. The job description of the nurse was to look after the clinical and administrative aspects of care, to complete data entry and connect with the doctor on TM. The role of attendant was to support the nurse and look after the housekeeping and assist in the survey.
It was clarified by word-of-mouth as well as notices that severe emergencies could not be handled at the clinic, other than first aid. Access for ambulance was tied up. A smart phone was provided to all clinics in addition to the TM hardware. This helped in continuous communication and support. It was also used for medical advice if the TM platform did not connect with doctor. The clinics were open from 1 pm to 9 pm on all days except Tuesdays. This was the time most suitable to the community. This was also a time when government dispensaries and Out Patient Department (OPDs) closed for the day.
All the clinics reported to the Hub. The Hub reported to the Command Centre (CC). The entire program was under the overall control of the CC. The Hub and CC were co located to ensure close control. The Hub was where the senior doctors sat. They were available for the same duration as the clinics. The doctor would be utilized for taking a daily report, taking TM calls, training of the nurses/attendants, and ensuring that daily reports and returns were complete in all respects. The doctor in turn would provide inputs to the senior management who would ensure that the clinics and Hub were supported and supervised. The senior staff would make fortnightly visits to the centers to understand ground realities.
Information was collected through public health surveys; interactions with villagers and local leaders; medical examination of individuals; feedback from patients, nurses, and doctors TM call records; and household surveys to understand the socioeconomic status of the community.
Characteristics of the site are given in Table 1:
Although planned for General Nurse Midwife (GNM) and attendant, we recruited Auxilliary Nurse Midwife (ANM)/General Nurse Midwife (GNM) as GNMs were not always available. The attendant job description was changed to a lady with computer skills as the nurses, especially in rural areas, were not computer literate.
The caregivers and the community were comfortable only with Marathi. This was especially true for written communication and data entry. We redesigned the forms to Marathi. These were filled by hand, and then, the data were entered at the Hub. Only OPD data were entered on the TM platform at the clinics.
Medicines had to be provided by us for the period of study. If not, then no one would come to the clinic. In spite of attempts, there was no way of accessing government dispensary for medication. This proved to be a huge cost.
Hemoglobin, sugar, and urine protein were checked at the clinic itself. However, the patients were not keen to pay for advanced laboratory workup. No laboratory was available in the vicinity in the villages.
Although permission for biomedical waste was applied for, no central system for disposal existed. So, the waste was incinerated and the sharps carried back to the Hub in sodium hypochlorite solution.
The clinic created a lot of interest and many would visit just to ‘see’.
At all sites, many patients demanded they be injected with medication or saline. It took a lot of convincing for them to accept our way of evidence-based care.
While the clinic scored well on timing, location, cleanliness, TM experience, and overall satisfaction, the suggestions provided were to continue services, have more medicines available, have ambulance available, and keep services free for laboratory. About 35% did not fill the feedback form. Since the forms were filled by the nurse often (patient was not very comfortable writing), the results may be biased.
While feedback on clinic services was excellent, few were willing to pay for services.
Household surveys measure many aspects of socioeconomic circumstances which in turn determine the health of the community (4). The salient features of survey are given in Table 2. Rulow had the least income, the least consumption of nutritious food, and also the least access to care and medication. Urslum ate eggs and meat most often although they had low milk consumption.
It was noted that mobile penetration was high.
Findings are based on N-1114 (Ruhi: 415 persons, Rulow: 353 persons, and Urslum: 346 persons).
Hardly anyone reported having an annual medical examination. This was true for all the three sites. Rulow was not interested in the written record of their health. Most could not read it in any case. In Urslum, the populace was more aware and wanted paper records unlike the other sites.
The prevalence of the main lifestyle-related illnesses studied is given in Table 3. The numbers reflect the socioeconomic status of the population The Rulow population had a high percentage of anemia and low body mass index (BMI), while those of Ruhi showed the emergence of obesity, diabetes, and hypertension along with anemia—a double burden scenario. It was seen that while in Urslum a fair percentage of patients were aware of their morbidities, this percentage was very low in the villages—many persons were unaware that they had diabetes or hypertension.
| Site | Anemia* | High blood sugar | High blood Pressure | Body mass index (BMI) < 18.5 | BMI > 25 |
| Ruhi | 71.43 | 18.63 (known cases 4.35) | 22.58 (known cases 5.48) | 22.12 | 27.64 |
| Rulow | 94.57 | 13.10 (known cases 1.1) | 2.09 (known cases 0) | 45.56 | 11.55 |
| Urslum | 11.94 | 18.48(known cases 9.8) | 5.18 (known cases 0.05) | 37.7 | 28.7 |
| Note: *Hb below 12 g% for women and below 13 g% for men. | |||||
The interest and the number of visits brought out that the need for primary care was prevalent at all three sites. TM alone could not meet the health needs. Last mile care delivery using a nurse was essential as none were competent to speak with a doctor independent of the nurse. Nurse was essential in explaining the condition, giving medicines, creatin records, follow-up, first aid, etc.
Prevalence of chronic, lifestyle, and nutrition-related ailments reflected the social fabric of the community. The social determinants clearly affected morbidity patterns (5). Income, education, occupation, social class, gender, race/ethnicity links, and influence in the socio-political context and material aspects are the determinants of disease, and they should be studied before setting up any health facility.
The expectation of care is different at the grass roots. The individual demands immediate relief from complaints. Concerns for privacy, health records, data security, or preventive care are minimal. As an example, it is difficult to communicate that the complaints are due to underlying anemia and malnutrition, and the treatment lies in nutritious food. The advice to eat nutritious food cannot be followed by the patient as he /she is poor and does not have the spending power needed to consume nutritious food. Taking tablets and supplements for 3 months or asking for further investigations is impractical as there is an unwillingness to pay the cost in terms of visits, medicines, or referral. Public health services must go hand in hand to provide comprehensive care. A more holistic model of care, rather than one focused toward specific ailments (specialist driven), may work better in these settings.
TM worked best in rurban and urban settings. This means that the really backward communities may not be able to access TM care, as it demands a basic level of infrastructure. How to provide care to very backward communities needs to be studied.
Recovering of the cost of running TM-enabled clinics, in a background of lack of support services, infrastructure, and unwillingness to pay, is a challenge. How to fund the TM clinic in the long term needs to be studied in this backdrop. The cost of care is reduced if there are at least five spokes to a Hub.
Timings of the clinic must suit the community. Afternoons and evenings suited all communities as otherwise they would lose a day’s wages. Also cattle and agriculture need work to be completed everyday with no holidays. Thus, clinic timings were kept 1 pm to 9 pm. Yet at Rulow, the clinic had to be closed by 7 pm due to lack of street lights and siting of the occasional leopard!
Maintenance of computer and med equipment is difficult in rural areas. Erratic voltage also affects equipment. This also applies to sourcing consumables.
Systems for statutory compliances are weak, and one has to innovate as one goes along.
Access to medicines, laboratories, and hospitals is dependent on the location. Ruhi and Urslum had access but Rulow did not. The government agencies do not meet all the needs of the people.
While local healthcare personnel are scarce and not as proficient, if available they are the most suited to the local environment and have a good connect with people. Most nurses don’t know how to use computers. Addition of a computer technician increases cost. This is more so in rural settings. Once personnel are recruited, they need to be given exhaustive training. The doctor at the Hub is the key to provide training. It was noticed that nurses learnt as the patient spoke with the doctor and he provided advice. Cost of training needs to be factored in.
Last but not the least, the local political organization is important and must champion the TM center. At Urslum, since there was no organization, it was very difficult to find a place to run the center. Security of the clinic was also an issue.
There is published literature that also talks about some of the above issues especially in developing countries—Concerns regarding costs of TM are amplified in the absence of funding from government or other healthcare organizations. Infrastructure challenges such as unstable power supplies, insufficient communication networks, and inadequate or unreliable internet connectivity with limited bandwidth, as well as a lack of human resources with the necessary technical expertise all limit where and to what degree TM initiatives can be applied (6).
TM is difficult to run reliably in really backward areas, yet they need care the most! Last mile care delivery along with public health initiatives is needed as TM cannot cure all ills. A model that connects with local government healthcare organizations for support and uses existing government infrastructure would be the key to sustainability.
Limitations of the study are that the period of data collection was 4 months. The study was in one state of India, so the applicability of the findings to other states/countries may vary.
The recent TM guidelines by the Government of India (7) are likely to see a sudden increase in the number of TM clinics and teleconsultations. This study details our experience in setting up clinics with the hope that others may benefit. Similar studies in different geographies will help in better planning and effective delivery of TM-enabled healthcare.
The authors acknowledge the support received from Ms Leena Deshpande, Head CSR, Bharat Forge, India, and her team in the conduct of the study.
Bharat Forge, India, supported the study with resources.
Both authors contributed to drafting the work or revising it critically for important intellectual content, and each gave the final approval for publication.