How to Transition to a Fully Remote Healthcare Provider

Jun 18, 2020 • 12 min read

Healthcare software solutions development in 2020 and beyond

The digital healthcare market is booming – in its 2019 report, Acumen anticipates that the market will reach a whopping $511 billion by the year 2026. Hence, it’s not an exaggeration to say that the future for digital medicine and remote healthcare looks bright.

Medical institutions and professionals are already using a whole range of software to support their day-to-day operations. Some solutions include management software like Electronic Health Records (EHR) and medical research software, as well as sophisticated programs like AI-powered chatbots and automated resource management systems.

Naturally, automation in medicine isn’t about replacing doctors – rather, it’s about solving the problem of limited human resources, all in a time- and cost-effective manner.

In the following article, we’re going to discuss, step by step, what can be done by healthcare providers to embrace digital solutions and custom medical software and transition to a fully remote service. We’ll also share examples of AI-driven healthcare solutions that can help your business become an innovative healthcare solution.

How to automate healthcare – a step-by-step approach

Step 0: Define processes and resources for optimization

Before you commence the transition into a remote healthcare provider, you’ll need to define the processes and resources that require optimization.

Here are the things you need to do:

Consider what your business goals are (for example: improved data management, increased patient volume, reduced patient wait time, etc.)
Think about the tasks and processes that will help you achieve your goals
Brainstorm and propose specific steps and solutions that will help your business meet its goals

Step 1: Select a data warehousing solution and specific services for data analysis

While the most healthcare providers have access to centralized databases – for example to manage medical inventory, patient, and employee data – most systems aren’t integrated with one another. This approach is counter-effective, which is why it’s necessary you implement one platform to enable the storage and analysis of all data.

Now, in order to centralize all your data, you’ll likely need to invest in infrastructure. You’ll need servers to calculate, regulate, and store data – which requires a significant financial investment.

For most healthcare providers, two major areas need to be addressed:

Storing large amounts of data (health & medical records)
Analyzing the data to make predictions based on them (i.e. big data processing)

One way of doing it is by building data warehouses, buying equipment, and hiring people to maintain it. Alternatively, you can use a cloud-based solution, which is more cost-effective.

What are the advantages of going with a cloud-based approach?

It doesn’t require a large cash investment (i.e. no facilities, equipment, maintenance)
It’s scalable (data warehousing solutions adjust to your changing requirements as you grow)
It’s accessible from any place with an Internet connection
Backups and copies give you the peace of mind in case of small accidents, like power outages or natural disasters like earthquakes or floods.

Here’s an overview of the most frequently used solutions:

Step 2: Select an electronic health record software (EHR)

EHR software in many ways resembles a CRM, only adjusted to the medical industry. It stores information on your patients – for example, their treatment, consultation, and prescription history.

The two most popular types of EHR software are:

Electronic patient record software (EPR) – used internally by hospitals to store and process their patient information.
Electronic medical record software (EMR) – used to store data such as medication dosage, past and planned procedures, or data on the patient’s recovery course.

There are plenty of benefits of using an EHR solution, most importantly, they:

give you secure access to patient records,
reduce the chances of medical errors,
have a positive impact on the quality of medical care.

Step 3: Set up a smart ETL pipeline for your data

Now that you have hardware and an EPR/EMR system online, it’s time to automate your data collection and formatting process. You’ll need to set up an ETL pipeline to extract, transform, and load data. As Wikipedia defines it, “an ETL pipeline is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s)”.

And so, ETL ensures various data are:

extracted from different sources,
transformed to the agreed, specific format and,
ready to be loaded (served) for analysis.

Once you establish the type of the database (Relational or NoSQL) in which you want to store your medical records, you’ll need an automated pipeline to ensure you keep all the data you may need in a unified format. This way, the data you collect can be further used by other analytical systems to benefit you and your patients.

Setting up an ETL pipeline facilitates a lot of operational work. Among others, it automatically identifies the file format, loads your data, visualizes it, and eliminates human errors.

Step 4: Migrate your current data (if applicable)

Once you’ve established your ETL pipeline, you should use it to fill your data warehouse with historical records (for example, any patient history you might have in a paper archive or another system).

As a result, all your historical data will be standardized and digitized.

It’s important to note that this might be the most critical part of the process, and you need to carry it out thoroughly.

Migrating your data, especially if some of your records are stored in paper form, will require a large amount of manual work and data engineer guidance.

Step 5: Pay attention to security and safety

Privacy is a critical concern – especially in the aftermath of huge data breaches and the introduction of GDPR. It’s crucial that you protect your patient data from being identifiable by unauthorized parties.

Privacy is often deﬁned as having the ability to protect sensitive information about personally identiﬁable health care information. Meanwhile, security is typically deﬁned as the protection against unauthorized access.

Several actions can be taken to ensure data integrity (i.e. protecting data from being tampered with, stolen, encoded or destroyed) and its availability (i.e. keeping a system of backups and warehousing solutions to prevent that data is lost in case of a major emergency like an earthquake).

From a technical standpoint, what are the key areas of security in healthcare?

Policymaking and establishing authorization requirements to ensure that patients’ personal information is collected, shared and utilized in the right way.
Preventing data breaches, hacker attacks, ransomware (i.e. encrypting or blocking access to data for profit), by utilizing necessary technical measures (SSL certificates, data encryption, and data workflow audits).
Making sure that you’re GDPR-compliant.

Paying attention to security is crucial for two reasons. Firstly, to avoid huge legal and financial consequences if your software gets reported to EU or national authorities. Secondly, to avoid losing access to data that is crucial for business operations.

Step 6: Choose additional services to implement data-driven management

Data-driven decision management is a business approach where decisions are based on verifiable data. Nowadays there are various AI-based solutions, developed for the Industry 4.0, which help medical institutions optimize their operational processes and reduce the costs.

These solutions include:

Predictive analytics – used in healthcare, for instance, to predict the workload during seasonal outbreaks
Predictive maintenance – planning typical maintenance to minimize the impact of an event on the healthcare facility
Inventory management – AI systems that help to manage medicament and material stock

These could be based on the above-mentioned analytical solutions integrated with services like AWS EMR, AWS Athena, GC BigQuery, and Azure Databricks. They are general-purpose, big data and machine learning “virtual workstations” containing tools that can help create any system driven by big amounts of data and requiring scalable computational power for machine learning models.

Step 7: Select additional services for improving the patient experience

Going remote doesn’t mean compromising on the patient experience. Remember that medical professionals are still responsible for the services they offer, regardless of the communication channel!

In order to make sure that your remote services still guarantee a good user experience, we recommend using the following.

AI-based solutions:

Chatbots: they take the burden off doctors by providing automated answers to common patient concerns. Doctors can thus focus on speaking to patients whose questions can’t be easily answered and require medical attention.
Diagnosis software: Medical diagnosis software lets doctors consult their patients’ symptoms or diagnoses with other medical professionals. Such tools use machine learning (ML) and deep learning (DL) algorithms to analyze anonymized patient data to generate a list of potential diagnoses. Diagnosis results may be exchanged and confronted with medical specialists. Here are some examples of how diagnosis software can be leveraged:

Identifying patients at risk of developing Alzheimer’s disease – thanks to structured and unstructured EHR data, machine learning algorithms can accurately recognize patients prone to developing Alzheimer’s and related dementias.
Skin Cancer identification – Stanford University has developed artificial intelligence that is trained to distinguish harmless skin tags from malignant skin cancers.

Medical prognosis software: in some cases, ML/DL algorithms can use historical data to predict future events (naturally, with a certain level of probability). For instance, these algorithms are used to predict pathogen spread dynamics (so called ‘epidemiological models’). Here are some great examples:

Predicting seasonal flu: A Self-Adaptive AI Model (SAAIM) has been used in China to predict and spot early outbreaks of the seasonal flu. The model analyzed data from 2012-18, and provided the most accurate prediction data, compared to other similar AI solutions.
Predicting severe cases of COVID-19: Hospitals around the US use an AI system developed by Epic to identify patients who will become critically ill after contracting COVID-19. The effectiveness of this method is still to be validated.

Health tracking software: There are plenty of innovative healthcare apps and IoT devices that leverage AI to monitor and analyze health from the comfort of your home. Health tracking software includes devices such as wristbands for sleep and pulse tracking, thermometers, and glucometers. Such devices allow tracking certain biomarkers for prolonged time periods, not only during examination, helping to set more valuable diagnoses.

It’s time to start thinking about your transition...

The COVID-19 pandemic has created a new reality – where face-to-face interactions need to be limited. This situation has affected all spheres of life, especially our health and access to medical care, so transitioning to a more remote healthcare model seems like a natural thing to do.

Not only do innovative healthcare apps allow patients to access medical care quickly by reducing patient wait time, but they also help medical providers manage and analyze medical data to provide better diagnoses and treatment.

In order to transition to a remote healthcare provider successfully, you’ll need to embrace a step-by-step approach. Among others, you’ll have to decide on the right tools and processes that will serve your healthcare business best.

The technology is here, and you have plenty of options to choose from. Consider implementing EHR software, cloud-based solutions, and an ETL pipeline to unify your data, make your processes more efficient and flexible and to produce higher quality services at a reduced cost.