Key cost considerations in the build vs. buy debate for healthcare analytics
Picture this: a healthcare system is growing at a rapid pace, and its data is ballooning just as quickly. As more patients, more money, and more data comes into play, administrators and executives must make critical decisions.
Whether it’s goal-setting or monitoring progress on existing initiatives, it’s almost impossible to make educated choices without good evidence to back them up.
Data analytics enters the picture, and decision-makers face the classic build vs. buy conundrum. In this article, we tackle some basic premises, like: should a healthcare system identify what they need and make it from scratch? Or should they partner with vendors who already offer solutions? Below, we go over the framework, how it’s applied in healthcare data analytics, and what questions to ask to ensure you take the right path.
What is build vs. buy?
Build vs. buy is a decision-making framework that exists in product development across industries and fields. It’s shorthand for a choice that stakeholders make time and time again: does a tech team dare build a system from the ground up? Or do they take a chance on an outside solution, trusting a vendor to execute on their timeline, to their standards?
There are arguments in support of (and against) both approaches, but an organization can follow these steps to discern the best path for their use case.
- Clarify: So you’ve discovered the status quo is no longer an option. What is the breaking point, and why now?
- Justify: You’re aware of a major hurdle that’s slowing down your team. Knowing that, what will solving it do for efficiency? Cost savings? Time recouped? The justification can be qualitative or quantitative, but it needs to make the intervention of new software or services worthwhile.
- Solidify: Gather evidence to support the need for new technology, and research potential solutions. Comb through reviews on sites like KLAS and ask trusted peers outside your institution. Fact-find so that you can cost-compare and narrow down the best options in Step 4.
- Consolidate: Armed with lots of information, you can narrow the field to the best contenders in this step. Side-by-side, break down the cost of building an in-house fix versus collaborating with another vendor, digging deep into what each course of action can offer.
Consensus is hard to come by in build vs. buy analysis
Even after you’ve addressed that last step (consolidating choices), with a set of viable vendors or a plan to forge ahead in-house, there’s a potential road block to overcome. Getting every stakeholder at a healthcare system on-board with either route can take ages, which is why it’s so important to have evidence and information at your disposal.
Think about the different roles that can make decisions in a hospital, for example: a CIO might feel their team is overburdened with existing tech issues, while a CAO might need capabilities that would take ages to build from scratch.
With different priorities come different opinions on the best path to take, so leave time for extensive research and discussion. As you’re compiling information, try to answer these stakeholders’ questions preemptively, addressing issues like interoperability, cost, rollout time, and future staffing needs.
You say hello, I say buy, buy, buy
Don’t let the phrase fool you — even building comes with an array of costs. It’s easy to look at the preliminary considerations above and think, “Why pay for external labor when we’ve already got an incredible team in-house?”
We’ll get to that question in the course of this article, but it’s important to note that few teams can construct every bell and whistle necessary to power advanced healthcare data analytics — often, the solution they arrive at is stitching together a combination of vendors and solutions made inside the organization.
Even the titans of healthcare are paying a cost to “build” — that just comes in the form of in-house experts’ salaries, and staffing large groups of designers, engineers, and analysts.
Cost: the final boss of healthcare data analytics
Like the warrior on the last screen of a video game, cost is the most important (and often most difficult) hurdle in build vs. buy decision-making. That’s because it’s not straightforward — you’ll spend money building from scratch, and you’ll certainly spend it contracting with vendors. Sometimes, the two amounts are roughly equivalent, or unexpected expenses crop up where you don’t expect them.
Where certain truths are obvious — multiple contracts = more money — others can slide under the radar. For example, even if you buy a platform, you’ll probably need to budget for some customizations to build by an in-house team. Below are some additional key considerations, so your planning and budgeting isn’t derailed by unanticipated costs.
Four key cost considerations in build vs. buy
- Time to value — We’re not the first to argue that time is money. But with a bit of investigating and math, you can put a number against those hours (or months, or years). Selecting and procuring a vendor can easily take two months with a dedicated team investigating and comparing, and the actual creation of infrastructure can span another eight months or more. Consider the urgency of what it is you need. Could a plug-and-play platform deliver immediate efficiency? Or is this a simmering issue that’s been going on for ages, and doesn’t require an immediate fix?
- Operating expenses — Beyond vendor contracts, other expenses will arise. Managing multiple vendor contracts will need dedicated employees and oversight. Hiring software engineers also proves especially steep — base salaries can start at $150K, and competition is fierce. Add to this the cost of storage for massive data, particularly if you’re not using a vendor, plus staff to handle software maintenance. Additionally, internal stakeholders will have to determine and sign off on money for researching and developing the solution.
- Hiring and staffing — If you thought the build vs. buy analysis was speedy, here’s the reality check. Staffing is a major reason to pause and make sure you’re on the right track. On average, it takes 60-65 days with a full recruiting team to adequately staff an in-house group for developing custom data analytics software. Tech companies are in the same choppy waters, fighting for great hires (and offering competitive salaries). Think of the many power players a healthcare system would need to recruit — software developers, data analysts, IT experts, and help desk staffers, just to name a few. The financial picture for contractors is just as bleak — their hourly rate can range from $175 – $210.
- Tech maintenance — Will you be using the cloud, or hosting servers on the premises? Who’s using the data, and how will it be organized? What system will you use for business intelligence reporting, and how will you make sure everything is interoperable?
Health and research analytics for insight and action
The costs can be overwhelming, but it’s important to remember what your organization gets in return. Where legacy systems can easily store or collect data, that’s not the same as gleaning insights. Once you have additional context, the data paints a detailed picture, helping guide decisions across your system.
It’s difficult to put a dollar amount on serving communities in need, or reaching a population that’s otherwise disengaged. It’s easy, however, to measure the huge impact of preventive care, proactive outreach, and increased operating efficiency. With a more powerful business intelligence and healthcare data analytics resource at your disposal, there are few goals out of reach.
Discover healthcare data analytics that power progress
Whether you build or buy, your data analytics should power meaningful results, from bettering an individual patient’s life to aiding a larger population. Tune into Arcadia’s webinar to learn more about this decision-making framework in healthcare.