One in three Nigerians abandon healthcare because of cost. MyItura tried solving this with software, then realised the real problem was money.One in three Nigerians abandon healthcare because of cost. MyItura tried solving this with software, then realised the real problem was money.

Day 1-1000: ‘Nigerian hospitals wouldn’t buy our software. So we started paying for their patients’ care’

2025/12/13 16:40

Shina Arogundade spent five months living with tooth pain because his insurance wouldn’t cover the full ₦120,000 ($82.62) for extraction. That experience would eventually reshape his entire company.

In April 2022, Shina Arogundade’s family lost their doctor of 17 years. By September, his father, who had battled chronic hypertension successfully under that doctor’s care, was dead. Five months. That’s all it took.

“His drugs were changed, the way he was treated was changed,” Arogundade recalls. “It was one complaint to the other. The experience left a bad taste in my mouth.”

The problem was clear – Nigerian hospitals operated in silos. No interoperability. No shared records. Doctors treat patients in isolation, sometimes ordering the same tests twice in one week. Arogundade once heard about a woman who nearly died because a doctor changed her diabetes medication dosage without knowing her history.

So in January 2023, Arogundade, who had previously co-founded a fintech company called Trade Lenda, launched MyItura, a digital health platform aimed at making health records interoperable across Nigeria’s fragmented healthcare system.

Three years later, MyItura is providing healthcare financing and preventive telemedicine services to Nigerians.

The EMR dream meets Nigerian reality

The vision was straightforward: build an electronic medical records (EMR) system that would allow hospitals, labs, and pharmacies to share patient data seamlessly. Patients would own their records. Doctors would make better decisions. Healthcare would finally enter the digital age.

“We tested the market, did customer interviews,” Arogundade says. “That was not going to work.”

“Most hospitals did not have the necessary finance to deploy the tools they felt were expensive,” Arogundade explains. “The key problem was not that they wanted to protect patient information. It was costly.”

There was also the cultural barrier. Older doctors accustomed to ‘pen and paper’ weren’t eager to start typing patient notes. The younger generation might be ready, but they weren’t the ones making procurement decisions.

MyItura had built a solution to a problem hospitals acknowledged but wouldn’t pay to solve.

Adeoluwa Ogunye (L) and Shina Arogundade (R), co-founders of MyItura

The first pivot: Building accessibility to get records

If hospitals wouldn’t adopt EMR directly, MyItura would have to get creative. The team pivoted to building accessibility tools: telemedicine platforms, AI-powered transcription for doctor-patient conversations, and a lab testing booking system.

The logic was if you can facilitate healthcare access, you can capture records as a byproduct.

They launched telemedicine APIs that other startups could integrate. They gave hospitals without websites a platform to conduct virtual consultations. They built a marketplace where patients could book lab tests and have phlebotomists come to their homes.

“With accessibility, we could then get records,” Arogundade explains. “When a patient and doctor had a conversation, AI could transcribe it, summarise it, help the doctor create notes, and help the patient keep a summary.”

The strategy worked—partially. MyItura started onboarding providers and patients. But the fundamental problem remained: Cost was still the bottleneck.

The lived experience that changed everything

Earlier this year, CCHub issued a call for proposals for its Digital Public Infrastructure (DPI) program. 

For Arogundade, the proposal landed at the perfect moment, strategically and personally.

Years earlier, he had needed a surgical tooth extraction. His insurance covered ₦20,000 ($13.79). The procedure cost ₦120,000 ($82.75). He couldn’t afford the gap.

“I didn’t remove that tooth until about five or six months later, trying to gather that money,” he says. “I was living with that pain. They gave me all sorts of things to pour into that tooth. Every night was a new set of pain.”

He had insurance. He had a job. And he still could not afford timely care .

“Because I had lived that experience, I know how painful it is to abandon care for something that could end up being catastrophic,” Arogundade says. “I felt this is something that should be solved for.”

The credit guy returns to credit

The timing was almost poetic. Before MyItura, Arogundade had worked in banking as a credit analyst, writing credit policies for banks. He’d co-founded Trade Lenda, a fintech focused on credit. His entire professional background was in lending.

“When I got the MediLoan idea, it felt like, ‘This is it,’” he recalls. “I’ve been doing healthcare for the last two years, but I have considerable knowledge around credit. This is an idea that fits perfectly.”

In December 2024, MyItura launched MediLoan, a ‘get treated, pay later’ healthcare financing product. Patients can access up to ₦200,000 ($137.32) in credit to cover medical expenses, with the payment going directly to healthcare providers, not to patients.

The product integrates via API, similar to how Paystack works for payments. Providers can add a “checkout with MediLoan” button. Patients click, get approved within 24 hours (or 30 minutes if the provider has integrated the API), receive treatment, and repay over time.

The pilot launched in November 2025. MyItura’s goal is to reach 750 users before a full rollout in February 2026.

Get The Best African Tech Newsletters In Your Inbox

Subscribe

Why everyone said no, and why MyItura said yes anyway

Healthcare financing isn’t new as a concept. 

“Banks will not do it. Microfinance banks will not do it,” Arogundade says bluntly. “There’s a lot of risk. But it can also be de-risked. I think it’s a reason to find ways to de-risk it.”

The risk is real. What if someone borrows for treatment and dies? What if repayment rates are catastrophic? What if the market isn’t ready?

But Arogundade argues the risk of inaction is worse.

“One in three people abandon care because of cost,” he says. “Someone with simple malaria that ₦10,000 ($6.89) or ₦20,000 ($13.77)  should treat, they go to the hospital, that money is not available. They abandon it. They go back home. They use agbo. It affects their kidney. Catastrophic outcomes, instead of a simple malaria drug that just treats them.”

Healthcare financing addresses the meta-problem: People aren’t abandoning care because they don’t want treatment. They’re abandoning care because they can’t pay for it.

The full-circle strategy: Money unlocks software

Here’s the elegant part, healthcare financing might be the key that unlocks MyItura’s original vision of EMR adoption.

If hospitals and labs have financing, they can afford to deploy digital tools. If patients have financing, they can afford to seek care. If both sides have liquidity, the entire ecosystem can digitise.

“If providers have that financing, if they have the liquidity necessary to deploy tools, then the whole electronic health records thing becomes more palatable,” Arogundade explains. “They are more willing to listen to you.”

MyItura is currently building out its APIs to make them available to other healthtech companies. They’re onboarding student ambassadors from medical schools to train hospitals on digital tools and prepare the next generation of doctors to adopt EMR systems from day one.

The team has grown to 13 people – 60% women, spread across tech, business development, operations, and research. 

What’s next: The 10-year vision

Arogundade’s vision for healthcare in Nigeria is simple: fewer hospital visits, more home-based care, and zero anxiety about cost.

“Things that can be done at home will be done at home,” he says. “First triage with doctors will happen at home. Pathology tests will largely happen at home. The way Chowdeck delivers food today, healthcare will also be delivered at home.”

And when people do need hospital care? “They will no longer be scared of the cost. It’s going to be, ‘I’m getting treated, and I’m sure MyItura will be there for me, and I can pay back later comfortably.’”

The path from EMR platform to a healthcare financing company wasn’t planned. It emerged from market rejection, personal pain, and the realisation that software alone can’t solve systemic problems when the system can’t afford software in the first place.

For MyItura, the lesson was painful but clear: Sometimes the infrastructure you need to build isn’t the infrastructure you thought you were building. Sometimes you have to finance the infrastructure before the infrastructure can exist.

Recommended Reading: “You need believers more than résumés”: Day 1-1000 of Pharmarun

Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Share
Medium2025/09/18 14:40