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Five considerations for a healthcare IT platform

By Linnie Greene, Staff Writer at Arcadia
Updated: February 22, 2023 Posted:
Data Interoperability and Integration Healthcare Analytics Predictive Analytics

A look at which features impact the decision to build vs. buy in healthcare IT platforms

In the third installment of our build vs. buy in healthcare series, we’re diving into a critical part of the decision — features. Technology offers lots of bells and whistles, but which ones matter? And which ones will help healthcare organizations hit the quadruple aim (better care, better patient experience, better provider experience, and greater financial efficiency)?

It’s essential to stay up-to-date, especially in a field changing as rapidly as medicine. It’s also important to get clear on the tools you need, which will identify the ones you don’t. A pretty UI is great, but for products built on top of healthcare data, the underlying data foundation needs to be ironclad.

Here, we’ll explore the most common features that matter to a healthcare system’s leaders, from security to platform capabilities. To catch up on other pieces of this decision-making framework, read up on cost considerations and reaching consensus with multiple stakeholders.

Why healthcare systems need sophisticated data analytics

Healthcare has changed dramatically in the last few decades, and that pace won’t slow down anytime soon. Technology helps an organization stay up to speed, but more importantly, it allows for future-proofing, the acceleration that can transform a system from meeting benchmarks to exceeding them.

Data analytics platforms allow healthcare networks to integrate and contextualize a sea of data from many sources. They also provide a holistic view of patients, so instead of floating data bytes, providers see a coherent history.

Great analytics are also a way to improve outreach (text messaging to a particular group in need of vaccines, for example), and it scales alongside a business. Growth is inevitable as healthcare networks expand to serve larger communities, and powerful technology will allow administrators and providers to share information easily and securely.

Five considerations for a health IT platform

To stay competitive, there are five categories in healthcare data analytics that an organization should assess:

  1. Data orchestration
  2. Maintenance and security
  3. Platform capabilities and data processes
  4. Outcome enablement
  5. Setup

1. Data orchestration

Before your technological journey can begin, you need a map. That starts with data orchestration. In this preliminary phase, the goal is matching the data sources at your disposal with the solutions you're browsing and your future goals. In pursuit of this trifecta, you’ll audit the data that’s coming from multiple locations, combine and organize it, and make it accessible for analytic use.


  • Data bottlenecks: can you unclog them?
  • Data storage: is it stored uniformly, and can you impose better organization?
  • Interoperability: will the platform provide data that incorporates with the tech you already have, or will your in-house staff need to spend time cleaning up data so that it’s coherent?

2. Maintenance and security

Ask any CIO or IT team and you’ll quickly learn how much work goes into the management and upkeep of a healthcare network’s software and tech. From reams of tickets to ornery integrations, there’s a lot that can go wrong. So when you’re choosing a data analytics platform, maintenance should be predictable and security should be ironclad. In a field where personal data’s extremely sensitive, you can’t afford anything less.


  • Security information and event management: what happens if something goes wrong?
  • Offensive and defensive cybersecurity: will your vendor or in-house team get out in front of attacks, and respond quickly to unexpected attempts to breach the system?
  • Responsiveness: who’s on call to handle emergencies, and are they reliable?
  • Operations: how much manual labor and maintenance will go into keeping the machinery running?

3. Platform capabilities and data processes

The data already exists — what you do with it matters. Even if you can move your data easily with interoperability and trust in its integrity, that hardly matters if you can’t use a data analytics platform to find meaning in the information. Capabilities like natural language processing (NLP) and analytic engines for factors like risk or quality monitoring are the difference between trailing and outpacing your competition. Functions like this are the most powerful levers to provide better care and trim unnecessary spending.


  • EHR, claims, and ancillary data integration: can your solution pull and aggregate data from all these disparate sources?
  • Telemetry and data health monitoring: does your platform of choice actively root out poor-quality data, or identify where more context is needed?
  • Master patient indexing: can you use this technology to identify a single patient across different systems and data sources, for a full picture?
  • Robotic process automation: does your in-house system or vendor offer automations that will help your team recoup valuable time?
  • Scalability: what volume of data can this technology accommodate, and can it adapt to sudden growth?
  • Data cleansing: is there an established process for catching faulty data, and either removing it from a data set or transforming it into useful information?

4. Outcome enablement

Outcomes are one-quarter of the quadruple aim, a term coined by Thomas Bodenheimer, MD in 2014, and in any model, patients are priority #1. Tech that automates or accelerates internal processes is great, but that can’t come at the cost of improving outcomes. At its core, whatever decision an organization makes — be it internal build-out or an external vendor — needs to lead to the health of the population it serves.


  • Data visualization and presentation: once the data’s aggregated, can it be displayed and sorted into visuals that help make sense of trends?
  • Report distribution: does the platform surface shareable, meaningful insights that are easily accessible and consumable for stakeholders like providers and executives?
  • Patient engagement: does the technology assist with outreach, via automation or otherwise?

5. Setup

It may seem minor if a platform delivers a radical difference in outcomes, but don’t underestimate setup — it can consume a massive amount of time and resources, and it’s essential that you know who’ll be responsible for implementation at the outset. Will it be your in-house IT team? If so, prepare for other projects to take a back seat. Will it be an external vendor? Agree on a timeline at the outset, and calibrate expectations before work gets underway.


  • Configuration and deployment: how many resources will you need to allot to get the platform up and running?
  • End-user training: who will train staff on how to use this technology, and how long will that take?

High standards for high-achieving healthcare

Healthcare networks ask a lot of a population health or healthcare data analytics platform, and rightfully so — it’s not just a bottom line in the balance, but human lives. Though minor compromises might be necessary (no technology is perfect, after all), the right solution will map to each of these discrete categories, offering functionality that’s more than just bells and whistles.

Tune in to our build vs. buy webinar to learn how Scott Samways, VP of IT at Beth Israel Lahey Health, approached this conundrum as his team pulled together a data analytics platform that met their needs.