5 Things I Wish Non-Engineers Knew About Software Engineering
Author
Publish Date
Read Time
Datavant
July 25, 2022
Photo by Igor Starkov, courtesy of Pexels
As the Head of Engineering at Datavant, I often work with folks outside our engineering team. I recently shared this list with other leaders at Datavant to help them build better relationships with the engineers they work with regularly.
Running an engineering team is about balancing three kinds of work
First, there is new feature development.This is what our customers care about. We want to spend as much time on this as possible! This is the tip of the proverbial iceberg. Under the surface of the water are the other two kinds of work.
The second type of work is maintenance of existing features. This is the “tax” we have to pay to maintain a feature we’ve already shipped. It consists of increased testing costs, increased development time because of interactions with the existing code, and knowledge sharing overhead across the team. Often, this cost is not explicit, but rather is paid implicitly in increased development time for features.
When maintenance work gets too high, we can invest in refactors & other productivity improvements that will pay off in reduced maintenance costs. This is the third type of work.
Source code alone isn’t very valuable
The ability to modify code & create new code is critical. When a company invests in building software, it gets two resulting assets: 1/ the source code for that product, and 2/ business context in the heads of engineers (which is distinct from skill/career growth). The value of (2) is easily overlooked by non-engineers, and is often larger than the value of (1). A team that has built domain knowledge can ship faster.
A corollary is that you are continually balancing delivering work now and expanding the team’s future capacity to deliver (by building domain knowledge, investing in hiring, training, mentorship, etc).
Engineers require maker time
Writing code and fixing bugs requires large uninterrupted blocks of “Maker Time”. It requires holding lots of context in your head about the data structures in your program, and there is some overhead to establish that context. Paul Graham wrote the definitive essay on this. In this model, a 30-minute meeting doesn’t just reduce productivity in a 2-hr block by 25%, it reduces it by essentially 100% … it cuts it into two smaller blocks that are each too small to do this work.
Shipping code is a team sport
A lone engineer heroically building a product is largely a myth — an engineering team gives feedback via design reviews & code reviews. Even projects that would seem to be independent are often coupled together via source repositories, code-level dependencies, shared infrastructure, etc. Your changes can break a feature someone else was working on, or require another engineer to refactor their work to incorporate yours. Much of modern DevOps is about trying to minimize this dependence.
The first version is always the easiest
The overhead to ship increases as a project grows. For v1, there are no design constraints, backwards compatibility or tech debt. After you ship v1, you need to ensure new changes don’t break existing scenarios, maintain interfaces that your customers have gotten used to, and work around the suboptimal design decisions you made early-on. Testing overhead also grows as you frequently add new tests, and rarely remove existing ones.
To the engineers reading this, I hope this list equips you with talking points to build better cross-functional relationships. And to the non-engineers, I hope this helps you better understand your engineering counterparts. Datavant is hiring for engineers who are smart, nice, and get things done.
Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification
AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).
AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.
"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.
“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."
Building Trust in Privacy-Preserving Data Ecosystems
As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.
As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.
The Power of SDOH Data with Providers and Payers to Close Gaps in Care
Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.
Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
Example: CDPHP supports physical and mental wellbeing with non-medical assistance
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
Providers Optimize Value-Based Care Using SDOH Data
Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:
Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.
By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.
While accessing SDOH data offers significant advantages, challenges can arise from:
Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.
SDOH data holds immense potential in transforming healthcare and addressing health disparities.
With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.
Careers
Join us on our quest to connect the world's health data
.svg)
