Month: December 2022

By Lorri Markum
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December 20, 2023

A special webinar, now on-demand here, for hc1 Sendout Management™, presented by hc1 experts, reveals background information about hc1’s core platform — hc1 Lab Insights Platform™ comprised of hc1 Analytics™ and hc1 Operations Management™ all of which help inform workflow management, analytics and precision health through easy access, high-quality specific data.

hc1 experts shared information about why Sendout Management was developed and highlighted key features of the solution, noting how this unified platform ensures the ability to monitor sendout TaT to drive accountability and improve care, the ability to optimize pricing to reduce sendout spend and standardizing lab sendout testing processes. “Our goal with Sendout Management was to streamline and standardize sendout data, so lab managers have easy-to-understand reports and dashboards available at their fingertips,” Adam Sajewich, hc1 high value care director, stated.

An introduction and demonstration of the functionality of hc1 Sendout Management in real-time and shows what this solution brings to the hc1 Lab Insights Platform. The ease-of-use dashboards allow both at-a-glance information and quickly accessible, granular information through filter features, such as ordering location type, panel/test name, sendout lab name, ordering provider and more. You can also quickly download and export reports directly from the dashboard. Moyer highlighted the solution’s capability to automate and deliver KPIs or dashboards and send them to decision-makers instantly to empower strategic changes.

Sendout testing is one of the most significant expenses clinical and health system labs experience today, but sendout testing hardships aren’t new, as noted in the 2009 article, “Explosive Growth of Send Out Testing: Can We Curb the Monster?”, published by AACC. With more than 7 billion clinical lab tests performed in the U.S. each year providing critical data, the challenge to meet the demands placed on laboratory staff and health systems is extreme. hc1 Sendout Management helps labs gain the insights they need to assess reference labs’ performance regarding sendout testing, hold them accountable to SLAs and find the best price. 

“Managing Sendout Testing is one of the top requests we [hc1] have received from our customers [and the market] over the past five years. In conjunction with increasing costs and the lab staffing crisis, the lack of transparency and standardization has made it more important than ever to bring our hc1 Sendout Management solution to the market to serve our customers, providers, and patients alike,” presenter John Moyer, hc1 sr. product director said.

Attendees asked great questions:

  • “How does hc1 determine if performing labs are meeting the SLA TaT?”
  • “How is sendout cost, per test, derived to calculate total sendout spend?”
  • “What if we have multiple sites using different LISs or EMRs, how would hc1 handle that?”
  • “What if our lab uses miscellaneous tests per sendouts? Can we still track those?”
  • “Do you calculate the cost per test in an organization or would this information be provided?”
  • “What is the typical lead time from the signed contract to the launch of this product for a customer?”

Watch on-demand here, learn the answers to the above questions, and see the complete webinar.

hc1 Sendout Management is available to both new and existing customers. Contact us today to get started if you would like a custom demo.

hc1 was built from the ground up for healthcare and is cloud-based, allowing better cross-communication and access to data within a health system. We were made to enhance labs and are 100% tailored for healthcare. Our solutions help you realize the value in your data from billing codes, naming conventions, diagnosis codes, ICD-10 codes, specimen IDs and more.

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Lorri Markum is the marketing manager for hc1 Insights and has over two decades of corporate and nonprofit marketing leadership experience. Lorri specializes in B2B marketing and SEO optimization. Before joining hc1, she was the marketing manager for a nonprofit healthcare organization serving 40 counties throughout Indiana.

By Heather Stith
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November 28, 2023

In the recent article “Why Big Data Science and Data Analytics Projects Fail,” Data Science Process Alliance consultant Nick Hotz outlines common problems all data project teams face and the questions that need to be answered for a data project to succeed. Since 2011, hc1 has been helping health systems and laboratories analyze their laboratory testing data to improve patient care. Our approach to resolving data project problems for our customers is rooted in our core values. I spoke with hc1 data scientist Alex Karr, for more details about this approach.

Hire curious people and develop their talents

Hotz mentions the difficulty of finding people with the appropriate data skills in today’s competitive market. What he doesn’t say is that organizations can choose to invest in developing the skills of the people they already have. Karr is a great example of this kind of career growth. He started with hc1 as an intern writing training documentation for our business intelligence team. When he was hired full-time, he learned to write queries and develop reports as an analyst. He then used his deep knowledge of the hc1 database to move into a data engineering role with the data team to manage their ETL (extract, transform, load) processes. While continuing to work at hc1, Karr earned his master’s degree in data science and was promoted to his current position.

“For me, the natural progression was we’ve got all this data, how do we actually make insights out of it? Of course, one way is doing visualizations for a client, but what I was more interested in was machine learning, so, you know, how can we make predictions based on what we have,”  Karr said.

Be accountable for data quality

As the Data Science Process Alliance article points out, not having the right data is the clearest reason for data project failure. “Not having the right data is pretty critical, because we can work with what we’ve got, but it’s always gonna have limitations. We have to make sure that we recognize those limitations and try to account for them, ” Karr said. He explained some of the ways we do this:

  • Integrating data from multiple sources. Lab testing data comes from systems that are designed to collect the information necessary for reimbursement, not operational or patient care improvement, so there are likely to be gaps in the information that can be gleaned from it. For example, we might need to look at medical claims data to get full diagnosis information or add financial data to present a complete picture of lab operations.
  • Cleaning data. Our patient matching process de-duplicates patient records so that we can build data models from a more manageable, performant dataset. Standardizing the format of zip code and state data points, for example, is important in public and population health use cases that involve patient geography, such as evaluating the risk of COVID-19 spread or tracking changes in opioid overdoses.

Standardizing test names and abbreviations and comparing test codes, analytes, and units of measure to determine whether tests with similar names are in fact referring to the same test is an essential part of making sure that the insights we provide for test utilization and order volume are accurate.

  • Evaluating data for bias. When compared with U.S. Census data, our dataset reveals a slight overrepresentation of women, for example. Knowing that fact allows our data scientists to adjust data models to reflect patient gender ratios that align with U.S. population numbers. 

At this point in the interview, Karr started getting into concepts that reminded me that not all hc1 technical writers (namely me) are destined to become data scientists. He spoke about imputation and overfitting and data leakage. Mostly, I understood that a major part of building a machine learning model is figuring out which data points to include and which ones to exclude, a process called feature selection. He also stressed that it’s important to separate your training data from your testing data.  

Use a collaborative project management process

The Hotz article lists ineffective project management processes as a reason for data project failure and recommends the Agile methodology, which is what the hc1 technology department uses, as a solution. As hc1 practices it, Agile means small, focused teams of product owners, software engineers, business and data analysts, and data scientists work together in two-week-long sprints to complete defined blocks of work. Breaking up the work in this way provides the flexibility to incorporate customer feedback and adjust to changing priorities. It also shortens the amount of time required to deliver value. Frequent communication and collaboration are essential to making the Agile process work, as is keeping the focus on the problem the customer is trying to solve. Although data science doesn’t often fall neatly into defined deliverables—many sprints are devoted to research—Karr finds the structure and clear goals of Agile to be helpful, especially when he splits his time among different teams and projects.

Handle data ethically

As a healthcare technology company, hc1 safeguards the protected health information in our systems in accordance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA). We also have taken the extra step to become HITRUST Risk-based, 2-year (r2) Certified to demonstrate our commitment to managing information security risks and protecting customer data. The Health Information Trust Alliance Common Security Framework (HITRUST CSF) serves to unify security controls based on aspects of U.S. federal law (such as HIPAA and HITECH), certain state-specific laws, and other industry-standard compliance frameworks into a single comprehensive set of baseline security and privacy controls, built specifically for healthcare needs.

The (r2) validated assessment certification is a tailored assessment for the highest level of assurance that an organization may earn from HITRUST. 

hc1 was founded on the belief that every patient should be treated as a unique individual and that if labs could organize every individual’s information intelligently, they could personalize and improve care for all patients. Now, hc1 solutions optimize laboratory operations for thousands of locations and inform testing and treatment decisions for millions of patients. Click here to learn more about the hc1 Platform which has organized diagnostic data for over 200 million patients and processes more than 30 billion clinical transactions and 500 million test results per month.