The Patient Population at Homeopathic Outpatient Clinics across India: A Clinical Data Collection Study

Keywords

homeopathy - data collection - clinical data - rubrics - India

Introduction

Homeopathy, a widely used treatment choice worldwide,[1] utilizes highly diluted and potentized medicines to stimulate the body's healing mechanisms. With nearly 200 million users globally[2] and a significant following in India,[3] where it is preferred alongside other AYUSH therapies,[4] there is a clinical environment that is conducive to integrating homeopathy into mainstream health care.[5] [6] However, despite its popularity and positive impact, the scientific community frequently challenges its validity.[7] [8] To address these claims about its clinical impact and generate quality research evidence, systematic collection, compilation and digitalization of data recordings at health care centers is crucial. By observing patterns and trends of data generated at clinics, the usage and applicability of homeopathy can be more precisely understood.

Clinical trials can assess the efficacy, safety and cost-effectiveness of homeopathic medicines, whilst data collection studies, which are more representative of usual care, have their own relevance in understanding key aspects of epidemiological and clinical data.[9] Nevertheless, real-world data collection is susceptible to various biases, such as selection bias (errors in participant selection), information bias (mis-classification of data), recall bias (selective recollection), centripetal bias (the reputation of certain clinicians and institutions cause individuals with specific disorders or exposures to gravitate toward them) and detection bias (unequal event capture)—unlike randomized controlled trials that control such issues—but these biases can be mitigated through appropriate data collection tools and study methods.[10] [11] [12] The use of artificial intelligence-based data collection tools has begun to address these concerns to some extent.[13] [14] In turn, data collection software itself can introduce biases, such as practitioner's confirmation bias (selecting symptoms indicating a medicine for which he or she has an intuitive preference) and repertorization bias (use of varied and sometimes unvalidated repertorization software, and in a different manner by different practitioners). In one study, confirmation bias, lowering of symptom threshold, and keynote prescribing were identified as the most important sources of bias.[15]

Altogether, at present, there is insufficient literature about the variations within individual data collection techniques and, hence, in the outcomes as well. The current study focuses on collecting data from patients receiving only homeopathic treatment at outpatient clinics associated with homeopathy research units/institutes of the Central Council for Research in Homoeopathy (CCRH) across several Indian states. These clinics had faced challenges with manual and non-uniform clinical data recording styles, making it difficult retrospectively to analyze the recorded data for research purposes. The present study was thus planned to digitally record such data, and it involved research officers who collected and managed clinical data generated from the outpatient departments (OPDs) of the clinics. An online database program was used for prospective data collection.

Data collection studies related to homeopathy have been conducted in the past. These have been instrumental in understanding patients' profiles and their morbidity trends, such as a patient benefit survey conducted in 2001 at the Liverpool Regional Department of Homoeopathic Medicine in the United Kingdom.[16] The survey included data from 1,100 patients and utilized the Glasgow Homoeopathic Hospital Outcome Scale, a 9-point Likert-like scoring system that is now referred to as Outcome Related to Impact on Daily Living (ORIDL), to assess outcomes. The study also reported patient demographics, prevalent disease conditions, follow-up visits, and conventional medication usage. However, there was a lack of reporting regarding the specific homeopathic medicine prescribed and the process of repertorization. Another study in the UK evaluated the health changes observed in routine homeopathic care for 6,544 patients over 6 years at the Bristol Homeopathic Hospital.[17] The main outcome was measured with a 7-point Likert-type scale. A pilot data collection in all five homeopathic hospitals in the UK over a 4-week period collected data from 1,797 patients pertaining to their demographics, main medical complaints, patient-reported change in health using ORIDL and complementary medicine usage.[18] Other studies have used varying means of data collection, such as a database, Excel spreadsheet or hard-copy version of a spreadsheet, or standardized paper-based questionnaires.[19] [20] There have been several other initiatives as well for homeopathy data collection in Europe.[21] [22]

Whilst these studies have shed light on the characteristics of homeopathy users in different locations worldwide, further exploration of these aspects, particularly in India, is necessary. The current study aimed to understand the patients' profiles and ailments treated at the homeopathic outpatient departments (OPDs), while gathering preliminary information on the repertorization process for prescription. The principal objective of the study was to collect clinical data from OPDs across India, record patient-reported outcomes (assessed using a 6-point Likert-like scale) and identify the range and preponderance of medical complaints treated with homeopathy. The secondary objectives were to derive further insights from those real-world clinical data, including consultation trends and the frequency of the rubrics and medicines used.

Methods

Study Design

This was a multi-center, observational study for which data were collected prospectively through an online software program, using pre-formatted (repertory-based) symptom entries and algorithm-based references for prescription. Subsequently, a retrospective analysis was conducted based on the generated data.

Ethics-Related Matters

All procedures of data collection were in accordance with Good Clinical Practice guidelines and ethical standards as per the Declaration of Helsinki of 1975, as revised in 2013.[23] The study was approved in the 22nd meeting of the Institutional Ethical Committee of CCRH [1-3/2019-20/CCRH/Tech./22nd EC/536, dated 18th June 2019] and was registered with the Clinical Trial Registry of India (CTRI/2019/11/022068) on 20th November 2019.

Study Setting

The study was conducted at the OPDs of 14 institutes/units of CCRH, India: Drug Standardization Unit, Hyderabad; National Homoeopathy Research Institute in Mental Health, Kottayam; Anjali Chatterjee Regional Research Institute of Homoeopathy, Kolkata; D P Rastogi Central Research Institute of Homoeopathy, Noida; Regional Research Institute, Guwahati; Regional Research Institute, Imphal; Regional Research Institute, Navi Mumbai; Clinical Research Unit, Siliguri; Regional Research Institute, Agartala; Clinical Research Unit, Puducherry; Clinical Research Unit, Tirupati; Homoeopathic Drug Research Institute, Lucknow; Clinical Research Unit, Gangtok; Regional Research Institute, Puri ([Supplementary Fig. S1] [map of study sites], available in the online version). These are homeopathic clinics where patients visit for homeopathy treatment for one or more conditions and, unless advised so, they continue with their ongoing conventional medications for other co-morbidities. The data were collected for 1 year, spanning from November 20th, 2019 to November 19th, 2020, including the follow-up visits.

The investigators for this study are research officers stationed at each research center, all of whom are post-graduate homeopathic physicians with a minimum of 5 to 10 years of professional experience. They were trained in the study protocol and data collection software, thus ensuring consistency of data entry across research centers.

Data Collection

An internet-based software program, Vithoulkas Compass (VC), was used for electronic data collection, due to the feasibility of its use as per the requirements of this study. All recordings in the VC software were done under unique patient serial numbers. The investigators recorded patients' symptoms, including main complaints, physical and mental generals, and other characterizing symptoms during the initial visit. They recorded in the software every notable complaint reported by the patient and reached a diagnosis. During data analysis, these diagnoses were categorized as per their respective International Classification of Diseases 11th Revision (ICD-11) codes.[24] However, prescriptions were based on repertorial suggestions resulting from totality, which included both pathological and individualizing symptoms. Each symptom was recorded along with its degree of intensity as assigned to it by the prescriber, at the baseline, and then in every follow-up: this value ranged from 0 to 4 ([Supplementary Fig. S2], available in the online version).

During the follow-up visits, an assessment form was also filled in to record the treatment outcome, which was the change in the patient's state in comparison with the initial appointment. This improvement assessment form, embedded in the software, facilitated outcome assessment of each case during the follow-up, in comparison with the baseline consultation. The range of improvement based on the prescriber's assessment for each case varied from large improvement to moderate, small or no improvement, as well as “Remedy did not act as expected” and “‘Not sure” ([Table 1]).

Table 1 Prescription feedback

Effect	Interpretation
Large improvement/effect	Significant improvement or elimination of the chief complaint. Marked improvement of the general condition. Strong reaction with resurfacing of old symptoms.
Moderate improvement/effect	Moderate improvement of the chief complaint(s) and general condition. Clear reaction to the remedy, possibly prolonged, or resurfacing of old symptoms.
Small improvement/effect	Small improvement of some symptoms. Light reaction to the remedy. No general condition improvement.
No effect	Remedy did not seem to have any effect.
Remedy did not act as expected	Sudden new turn of events that the remedy is not expected to have/precipitate.
Not sure	Effect of remedy cannot be ascertained from the outcome.

The extracted data were anonymized with registration numbers, stored in Microsoft Excel spreadsheets and used to generate pivot tables for analysis.

The software stored all sorts of patient details: registration, chief complaints, case totality, repertorization, prescriptions, follow-ups and outcome assessments. The investigator had the flexibility to choose from various prescription methods, including flat repertorization, numerical analysis, and other forms of differential analysis (repertorization based on main keynotes, small remedy symptoms, case-specific differential symptoms and main differential symptoms). The software also played a facilitating role in data storage, retrieval and simplifying repertorization. It integrated an internet search, providing access to homeopathy-related websites and also Materia medicae by Boericke, Clarke, Allen and Kent. It is crucial to acknowledge that such features might influence remedy selection and case outcomes. The software served as a comprehensive tool for record-keeping of cases, decision-making in prescriptions, addressing biases and analyzing data.

Participants

The participants comprised patients of either sex, attending homeopathic OPDs and not enrolled in any research studies, a regular activity of these centers. Informed consent was obtained from each patient to utilize his or her anonymized data for analysis and publication.

Variables

The data were extracted under the following categories: case number, event number, event type (analysis, prescription, follow-up), event date, age, sex, case notes/main medical complaint, rubrics with intensities (0 to 4), prescribed remedy, and clinical outcome assessment. The data were further categorized under the following headings: number of follow-ups, symptom totality, morbidity profile (ICD-11 classification), prescribed medicines, most common rubrics, rubrics for the top three prescribed medicines, and clinical outcome assessment (assessed using the 6-point Likert-like scale that is embedded in the VC software—see above).

Broad groupings of the data were made for quantitative variables, such as age (categorized into children, adolescents, young adults, middle-aged, and older adults), sex, number of follow-ups, and morbidity profiling. The complaints were categorized as per the ICD-11 codes, used for classifying disease data: this was done because ICD coding is contemporary and easily integrated with electronic health records, as well as being generated from the software used in the present study. The commonly used rubrics and medicines were identified through pivot tables in Excel and categorized further to gain insights into individual and overall prescription patterns.

Bias

Experienced, post-graduate research officers trained for data collection recorded patient data in the software to reduce bias. To ensure consistency, online meetings with all the doctors were held regularly. The software also played a role in addressing confirmation bias that results from the practitioners' intuitive preference for a medicine, by producing a medicine chart based on non-intuitive, symptomatology-based scoring of remedies. However, bias related to the repertories or rubrics identification could have existed. The necessity of entering the intensity of every symptom/rubric during every follow-up could have helped in minimizing this bias to some extent.

Statistical Methods

For statistical analysis, Microsoft Excel was used to generate pivot tables, summarizing the extensive data into user-friendly tables and graphs. All incomplete entries were filtered out to maintain a fixed sample size. Trends were identified and visually represented using bar graphs and tables.

Results

A total of 2,811 patients visited during the study period, out of which 2,468 were new patients and 343 were solely follow-up cases. Among the new patients, the prescriptions of 2,200 individuals were obtained using the software. The rest of the entries did not mention the prescribed medicine in the software. Of those prescribed, 2,172 entries showed homeopathic medicines as the first prescription, while the other 28 showed that placebos were given as the first prescription. For those 2,172 patients, a total of 3,491 prescriptions were recorded (including follow-ups). Outcome assessment forms were available for 868 patients, based on one or more follow-ups, thus totaling 1,628 follow-up entries ([Fig. 1]). The number of follow-ups of each patient varied from 1 to 13, with 13 follow-ups being the maximum recorded for one patient, as depicted in [Fig. 2].

Fig. 1 Study flow chart.

Fig. 2 Trend of follow-up distribution (n = 868).

Demographic Data

Out of the 2,811 patients whose entries were registered in the software, 56.10% were females (n = 1,577) and 37.35% were males (n = 1,050); gender was not recorded for 6.55% (n = 184) patients. The highest patient count belonged to the age group 20–49 years, while older patients (above 60 years) were the least represented ([Fig. 3]). Age was not recorded for 273 patients.

Fig. 3 Distribution by age (n = 2,811).

Morbidity Profile

The medical complaints were classified according to the ICD-11 system and further grouped into 28 categories of this schemed coding. Some category names were modified for readability (e.g., “Infections of the foetus or newborn” to “Infections of newborn”). Complaints not falling into any of the 28 categories were mentioned as “Others”. Complaints of the musculoskeletal system were recorded as the highest, followed by skin and respiratory disorders ([Fig. 4]). The top 25 specific complaints are detailed in [Table 2], with joint pain being the most reported, followed by dermatitis/eczema, low back pain, cough and headache, consistent with the system-wise classification trends.

Table 2 Morbidity profile (n = 2,811)

No.	Disease condition	ICD-11 disease code	No. of patients	Gender			Age (in years)		No. of new patients	No. of follow-ups
				Male	Female	Unknown	Age ≤30	Age >30
1	Pain in joint	ME82	204	56	144	4	36	168	177	136
2	Dermatitis or eczema, unspecified	EA8Z	137	66	60	11	72	65	122	90
3	Low back pain	ME84.2	129	39	78	12	28	101	110	91
4	Cough	MD12	98	34	55	9	50	48	90	52
5	Headache, not elsewhere classified	MB4D	74	20	46	8	28	46	60	51
6	Asthma	CA23	72	29	34	9	28	44	69	21
7	Hemorrhoids	DB60	68	32	33	3	19	49	56	53
8	Type 2 diabetes mellitus	5A11	61	37	23	1	7	54	58	23
9	Localized abdominal pain	MD81.1	54	16	33	5	25	29	48	44
10	Polycystic ovary syndrome	5A80.1	51	0	50	1	42	9	42	17
11	Allergic rhinitis	CA08.0	47	22	23	2	25	22	43	29
12	Acute nasopharyngitis	CA00	45	18	26	1	24	21	41	24
13	Osteoarthritis, unspecified	FA0Z	44	16	28	0	3	41	26	39
14	Seropositive rheumatoid arthritis	FA20.0	42	4	30	8	21	21	40	2
15	Constipation	ME05.0	41	17	23	1	10	31	37	37
16	Gastroenteritis or colitis without specification of infectious agent	1A40.Z	37	22	14	1	17	20	34	9
17	Hypothyroidism	5A00	34	6	27	1	11	23	28	17
18	Urinary tract infection, site not specified	GC08	34	14	19	1	17	17	30	11
19	Acne	ED80	33	10	22	1	30	3	29	9
20	Gastritis	DA42	33	8	24	1	11	22	25	26
21	Essential hypertension	BA00	31	11	19	1	5	26	27	21
22	Dermatophytosis, unspecified	1F28.Z	31	14	16	1	13	18	28	10
23	Common warts	1E80	30	14	14	2	14	16	26	13
24	Functional dyspepsia	DD90.3	30	20	8	2	5	25	30	7
25	Anxiety	MB24.3	30	11	17	2	11	19	28	8
26	Others (357 diseases)		1,321	513	712	96	584	737	1,164	788
Grand total			2,811	1,049	1,578	184	1,136	1,675	2,468	1,628

Fig. 4 System-wise distribution of main medical complaints (n = 2,811).

Rubrics and Prescriptions

The frequently used rubrics for the most prescribed medicines were analyzed. “Desire for sweets” was the most common rubric, followed by “Desire for spices” and “Thirstless” ([Fig. 5]). Among the prescribed medicines, Sulphur ranked the highest, followed by Rhus toxicodendron, Natrum muriaticum and Nux vomica ([Table 3]). Sulphur was predominantly prescribed for skin disorders, Rhus toxicodendron for musculoskeletal issues, and Natrum muriaticum for genito-urinary disorders. The most frequently used rubrics of the top three medicines are shown in [Fig. 6]: interestingly, the rubrics “Desire for sweets” and “Constipation” were covered prominently by each of these three medicines.

Table 3 Distribution of top 20 homeopathic remedies prescribed across broad diagnostic groups (n = 3,491)

No.	Prescribed remedy	Mu	Sk	R	D	GU	ENM	Nr	M	C	N	E	In	F	Sl	B	V	AI	P	IF	O	Grand total
1	Sulph.	54	128	12	60	10	12	13	2	4	3	3						2			16	319
2	Rhus-t.	199	16	14	11	2	4	9	1	1		1	6								1	265
3	Nat-m.	26	29	17	26	36	16	27	4	10	2			1	2	1	1				3	201
4	Nux-v.	33	9	23	75	16		3	3	4	2				1						2	171
5	Ars.	8	26	77	24	12	5	4	5	2					1						4	168
6	Puls.	38	12	17	33	22	18	5	1	8		6		2			1		1			164
7	Phos.	14	12	53	19	13	21	6	9	3	5	1				4					4	164
8	Lyc.	17	10	16	50	23	9	6	1	10	3	2	3		1		1				8	160
9	Calc.	20	16	34	23	13	11	6	4		2							2	2	1	5	139
10	Sep.	14	36	5	9	17	7	7		4	1			1	1						1	103
11	Sil.	11	32	12	11	2	1	3	2			4	1								7	86
12	Bry.	42	2	13	10	5	3	7		1											3	86
13	Merc.	3	14	14	19	7	2	1	1		1	1										63
14	Thuja	6	15	6	10	12	3				6										3	61
15	Hep.		9	33	3	4						1	1								4	55
16	Calc-p.	14	5	15	4	5		4		2	3										2	54
17	Graph.	4	21	3	3	5	6	1		3										2	4	52
18	Caust.	16	6	4	2		1	11		2		2									2	46
19	Nit-ac.	1	9	4	20	5				4												43
20	Arn.	22	2		6	3		2	1				5								1	42
	Total	542	409	372	418	212	119	115	34	58	28	21	16	4	6	5	3	4	3	3	70	2,442

Abbreviations: AI, autoimmune disorders; B, blood-related disorders; C, circulatory system; D, digestive disorders; E, ear or mastoid process; ENM, endocrine, nutritional or metabolic diseases; F, feeling ill or fatigue; GU, genitourinary disorders; IF, infections of newborn; In, injuries; M, mental disorders; Mu, musculoskeletal system; N, neoplasms; Nr, neurological disorders; O, others; P, pregnancy, childbirth or the puerperium; R, respiratory diseases; Sk, skin diseases; Sl, sleep disorders; V, visual system.

Fig. 5 Distribution of most common rubrics used for prescription (n = 3,491).

Fig. 6 Prevalence of most frequent rubrics of top three prescribed medicines (n = 3,491).

Outcome Assessment

A detailed patient outcome assessment was done on every follow-up to compare the patient's present state with the baseline consultation. Of the 868 patients who visited for at least one follow-up, 86% witnessed some sort of symptom relief with the homeopathic treatment (11% reported large improvement, 35% moderate and 40% reported small improvement); 14% witnessed no improvement. There were no responses for the categories “Remedy did not act as expected” or “Not sure”.

Discussion

The present study reports the findings of data collection conducted in a routine homeopathic outpatient setting using software for case recording and data collection. The demographic data of the patients, such as predominantly younger age group of patients[25] and a higher proportion of female patients than male patients,[16] [18] [21] are comparable with the available literature. Whilst above 60-year-olds were the least represented in the present study, other data collections found the highest frequencies among the 46 to 60,[16] 49 to 64[17] and 40 to 60[18] years age groups. Such findings suggest that homeopathy users are not limited to a particular age group and that a wide range of patients utilize homeopathy across different settings.

Most of the patients had consulted for chronic disease, as was the case in another study.[16] In particular, the major diagnostic groups in the present study were musculoskeletal complaints, followed by skin and respiratory disorders, consistent with other studies: musculoskeletal conditions, followed by dermatological conditions, respiratory diseases, chronic fatigue syndrome and post-viral fatigue syndrome, gynecological problems, headaches, psychiatric problems, and gastrointestinal diseases[16]; dermatology, neurology and rheumatology[17]; and eczema, chronic fatigue syndrome, menopausal disorder, osteoarthritis, and depression.[18] A survey conducted at the OPD of the Royal London Homoeopathic Hospital in the UK reported that musculoskeletal system problems were the most frequent diagnostic group.[26] Complementary and integrative medicine, as a whole, has also been found to be used most frequently for neoplasms and musculoskeletal diseases.[20] In the present study, there were few patients with type 2 diabetes mellitus who were younger than 30 years ([Table 2]); also, male diabetic patients outnumbered female diabetics. These trends are consistent with observed trends in diabetes epidemiology in India.[27] Polycystic ovary syndrome was also much more prevalent below the age of 30 years, which is in line with other studies.[28]

Furthermore, in our study, 62% of the consultations were follow-up visits, suggesting good patient adherence to the treatment; it is notably higher than the 45% reported in another study.[18] A positive response to homeopathic treatment was recorded for 86% of follow-up cases in our study. This surpasses the improvement or positive health changes earlier reported to be 76%,[16] 70%[17] and 50%.[21] Our numbers for large improvement, however, are lower than two of those earlier studies: 11% compared with 32%[16] and 25%.[17]

This study is the outcome of real-world data analysis from 14 health clinics in different parts of India. Such pragmatic-setting studies in homeopathy—including in the context of randomized controlled trials (RCTs)—are far from being numerous.[29] More case reports highlighting responses to homeopathic treatment,[30] as well as clinical trials designed specifically for this therapeutic modality, are crucial to show the potential of homeopathy.[31] There will always be a divide between the worthiness of RCTs conducted in a controlled situation in an often homogeneous sample of patients and real-world effectiveness studies from routine health care in the clinic setting. In particular, it is rightly argued that placebo-controlled RCTs often are not representative of the patient population encountered in clinical practice.[9] For observational studies, clinical data collection can be either prospective or retrospective in design and take place over a long time period: such real-world evidence has emerged as an important means to understand the utility of medical interventions in a broader, more representative, patient sample.[9] Thus, real-world data from the homeopathic clinics can be a valuable resource to understand the usefulness of homeopathy in different settings.

In homeopathic practice, the process of detailed history taking, case analysis and individualization-based selection of a medicine is an elaborate one. Recording these details electronically, in a standardized format, can save time, as well as enrich the homeopathy data pool through retrospective analysis of the recorded data in spreadsheet or database programs. The database programs are more user-friendly, but pivot-table analysis in spreadsheets is also gaining popularity for its ease and practicality.

In contrast to the modern methods of data collection, the historic clinical data of most of the homeopathic clinics in India lie dormant in disconnected, inaccessible repositories, hindering crucial linkage, analysis and meaningful interpretation.[32] Compared with the old standard practice of recording crucial details on paper cards, our study implemented a digital data collection system. This shift from unreliable, fragmented records to a comprehensive, electronic platform streamlined data analysis and ensured consistency. In addition, the symptoms could be recorded in the form of repertory rubrics, along with change in their intensity, during follow-ups. Further, the VC software assisted in repertorization, homeopathic medicine selection, and helped counter practitioners' bias in prescriptions. These factors highlight the advantage of digital clinical data recording for future analysis.

Whilst the use of software-based data collection has clear benefits, we also recognize its potential limitations. First, the fact that VC software is mainly based on Kent's repertory served as a limitation, since all the cases—whether or not with sufficient mental or physical generals—were repertorized using this software. Hence, the bias of repertory selection could not be addressed in the present study. Also, since entries were based on the software's pre-determined schema, the outcome assessment scale was not modifiable, thus providing no scope to make it more balanced to reflect clinical deterioration as well as improvement. Other more accepted outcome assessment scales, such as ORIDL and MYMOP, could therefore not be used, a limitation that must be taken into consideration in future data collection studies in homeopathy that use dedicated software.

Elaborate homeopathy interviews in busy OPD settings may result in incomplete data recording by doctors, pointing to the likelihood that more complete records would be obtainable in the in-patient setting. The ICD-11 classification of the disease in the present study was done retrospectively, which in some cases was difficult when the diagnosis needed to be supplemented by further details. In addition, possible confounders such as the absence of standard diagnostic criteria, spontaneous recovery in acute cases, recall bias of patients, and no record about concurrent conventional treatments, should be addressed in future work.

Thus, the learnings as well as the findings from this study can be a valuable source of information for subsequent data collection studies in homeopathy, including growing a substantive nationwide data inventory for outpatients and in-patients across India.

Conclusion

Homeopathy is prescribed in CCRH outpatient clinics for a wide range of ailments in people across India, with some degree of clinical improvement in most cases. With a large-scale systematic data collection such as this, useful information about the use and clinical value of homeopathy in India can be recorded to build a substantive nationwide data inventory over time.

Highlights

• • This data collection study of 2,811 patients was conducted with the aid of internet-based software in homeopathy clinics across India.

• • The most common complaints for which patients visited the homeopathic clinics were musculoskeletal, skin and respiratory problems.

• • The frequently used rubrics for the most prescribed medicines were analyzed.

• • Polychrests such as Sulphur, Rhus toxicodendron, Natrum muriaticum and Nux vomica were the most prescribed homeopathic medicines.

• • A positive outcome of homeopathic treatment was reported by 86% of follow-up patients.

• References

• 1 World Health Organization. WHO Global Report on Traditional and Complementary Medicine 2019. Geneva:: World Health Organization; 2019
• 2 Homoeopathy Research Institute. Homeopathy use around the world. Available at: https://www.hri-research.org/resources/essentialevidence/use-of-homeopathy-across-the-world/ . Accessed October 6, 2023
• 3 Prasad R. Homoeopathy booming in India. Lancet 2007; 370: 1679-1680
• 4 Rudra S, Kalra A, Kumar A, Joe W. Utilization of alternative systems of medicine as health care services in India: evidence on AYUSH care from NSS 2014. PLoS One 2017; 12: e0176916
• 5 Manchanda R, Koley M, Saha S. et al. Patients' preference for integrating homoeopathy services within the secondary health care settings in India: the part 3 (PPIH-3) study. J Evid Based Complementary Altern Med 2017; 22: 251-259
• 6 Ganguly N. Empowering patients with a “cafeteria approach” in healthcare. Times of India Blog. April 10, 2023 . Accessed October 6, 2023. Available at: https://timesofindia.indiatimes.com/blogs/voices/empowering-patients-with-a-cafeteria-approach-in-healthcare
• 7 Fisher P. Homeopathy and The Lancet. Evid Based Complement Alternat Med 2006; 3: 145-147
• 8 Ullman D. An analysis of four government-funded reviews of research on homeopathic medicine. Cureus 2021; 13: e15899
• 9 Blonde L, Khunti K, Harris SB, Meizinger C, Skolnik NS. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther 2018; 35: 1763-1774
• 10 Catalogue of Bias Collaboration. Carl Heneghan. Centripetal bias. In: Catalogue Of Bias. 2019 . Available at: https://catalogofbias.org/biases/centripetal-bias/ . Accessed October 6, 2023.
• 11 Roche N, Reddel H, Martin R. et al Quality standards for real-world research. Focus on observational database studies of comparative effectiveness. Ann Am Thorac Soc 2014; 11: S99-S104
• 12 Tripepi G, Jager KJ, Dekker FW, Zoccali C. Selection bias and information bias in clinical research. Nephron Clin Pract 2010; 115: c94-c99
• 13 Silberg J, James M. Tackling bias in artificial intelligence (and in Humans). McKinsey & Company. Available at: www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans . Accessed December 5, 2023
• 14 Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: a call for open science. Patterns (NY) 2021; 2: 100347
• 15 Manchanda RK, Miglani A, Chakraborty M. et al. Impact of bias in data collection of COVID-19 cases. Homeopathy 2022; 111: 57-65
• 16 Richardson WR. Patient benefit survey: Liverpool Regional Department of Homoeopathic Medicine. Br Homeopath J 2001; 90: 158-162
• 17 Spence DS, Thompson EA, Barron SJ. Homeopathic treatment for chronic disease: a 6-year, university-hospital outpatient observational study. J Altern Complement Med 2005; 11: 793-798
• 18 Thompson EA, Mathie RT, Baitson ES. et al. Towards standard setting for patient-reported outcomes in the NHS homeopathic hospitals. Homeopathy 2008; 97: 114-121
• 19 Rutten L, Smedley T, Ives G. et al. Data collection during the COVID-19 pandemic: learning from experience, resulting in a Bayesian repertory. Homeopathy 2021; 110: 94-101
• 20 Rotter G, Binting S, Teut M, Ortiz M, Willich SN, Brinkhaus B. Characteristics of patients presenting at a university outpatient department for complementary and integrative medicine. Complement Med Res 2023; 30: 279-288
• 21 Steel A, Cramer H, Leung B. et al. Characteristics of homeopathy users among internal medicine patients in Germany. Forsch Komplement Med 2016; 23: 284-289
• 22 Thompson E, Viksveen P, Barron S. A patient reported outcome measure in homeopathic clinical practice for long-term conditions. Homeopathy 2016; 105: 309-317
• 23 World Medical Association. WMA Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects. World Medical Association. Accessed October 6, 2023. Available at: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/
• 24 World Health Organization. International Statistical Classification of Diseases and Related Health Problems, 11th Revision (ICD-11). Retrieved September 15, 2023, from https://icd.who.int/browse11/l-m/en
• 25 Meier-Girard D, Lüthi E, Rodondi PY, Wolf U. Prevalence, specific and non-specific determinants of complementary medicine use in Switzerland: data from the 2017 Swiss Health Survey. PLoS One 2022; 17: e0274334
• 26 Sharples FMC, van Haselen R, Fisher P. NHS patients' perspective on complementary medicine: a survey. Complement Ther Med 2003; 11: 243-248
• 27 Pradeepa R, Mohan V. Epidemiology of type 2 diabetes in India. Indian J Ophthalmol 2021; 69: 2932-2938
• 28 Ganie MA, Rashid A, Sahu D, Nisar S, Wani IA, Khan J. Prevalence of polycystic ovary syndrome (PCOS) among reproductive age women from Kashmir valley: a cross-sectional study. Int J Gynaecol Obstet 2020; 149: 231-236
• 29 Mathie RT, Ulbrich-Zürni S, Viksveen P. et al. Systematic review and meta-analysis of randomised, other-than-placebo controlled, trials of individualised homeopathic treatment. Homeopathy 2018; 107: 229-243
• 30 Vithoulkas G. An innovative proposal for scientific alternative medical journals. J Med Life 2017; 10: 197-199
• 31 Gaertner K, von Ammon K, Fibert P. et al. Recommendations in the design and conduction of randomised controlled trials in human and veterinary homeopathic medicine. Complement Ther Med 2023; 76: 102961
• 32 Institute of Medicine (US) Roundtable on Value & Science-Driven Health Care. Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good: Workshop Summary. Washington (DC): National Academies Press (US); 2010 . Summary. Available at https://www.ncbi.nlm.nih.gov/books/NBK54290/