Recombinant Data Corp.

Recombinant partners with J&J team to build tranSMART

2010-01-20T11:54:00.003-05:00

Kevin Davies of Bio-IT World wrote the article “Running tranSMART for the Drug Development Marathon”, an overview of a translational medicine data warehouse project at Johnson & Johnson, led by Eric Perakslis, VP of R&D informatics, managed by Sándor Szalma, senior research fellow, and delivered by Recombinant during an 18-month implementation.

Davies wrote: “TranSMART helps investigators mine drug target, gene and clinical trial data to aid in predictive biomarker discovery, chiefly in immunology and oncology. Perakslis says it is an ‘amazingly advanced’ data warehouse that compares favorably with many such efforts he’s seen in the Pharma world.”

Davies highlighted the open platform approach by Recombinant: “Perakslis is currently having discussions [with Recombinant] about a Red Hat approach where a lot of the advanced analytics is made open source. ‘I’m not running a commercial software company,’ he says. He’s already offered the software to Guna Rajagopal, a colleague at the Cancer Institute of New Jersey. In addition, St Jude Children’s Research Hospital, UCSF and other centers are all considering the adoption of the i2b2-based platform.”

Unlocking HIT Potential

2009-12-28T12:32:00.002-05:00

Joel Amoussou wrote the blog post “Unlocking the Potential of Health Information Technology (HIT)”, a sensible perspective on reducing healthcare costs and improving quality through standardization, open source technology, and staff training.

Congratulations to Dr. Kohane

2009-11-17T14:15:00.001-05:00

The American Medical Informatics Association (AMIA) honored Zak Kohane yesterday with the Donald A.B. Lindberg Award for Innovation in Informatics for his work with Informatics for Integrating Biology and the Bedside (i2b2). Dr. Kohane is the director of the Children’s Hospital Informatics Program and Lawrence J. Henderson Professor of Pediatrics and Health Sciences and Technology at Harvard Medical School.

Statewide data warehousing

2009-10-12T08:13:00.006-04:00

Recombinant is based on the east coast, which is unfortunate for us as the folks in Hawaii never called us about their new statewide data warehouse. Nonetheless, they are deploying a system that is focused on both population health and management of infectious diseases. Let it be known that Recombinant is always available with useful expertise and technology for statewide implementations--especially when it involves on-site work in Maui or any other decent surfing spot!

The interesting part of the Hawaii warehouse project is the alert and monitoring system that manages the spread of infectious diseases. This is a major problem in areas with high volumes of tourism, and a great example of why a government needs to review the unique economic and social structure of their state when building a centralized healthcare data warehouse.

Hopefully other states and large metropolitan areas that are timid about the notion of a statewide initiative will consider Hawaii as a successful reference point. Progress can be made on the political, competitive, and technical fronts that ultimately produce collaborations that are in the best interest of every patient's health and safety.

Dan Housman
Managing Director, Analytical Applications

The price of standardization

2009-10-09T08:07:00.005-04:00

The article “EHR interface costs likely to plummet” written by Mary Mosquera, references statements made by John Halamka from a recent Healthcare Information Technology Standards Panel (HITSP) board meeting—the cost to interface EHR applications will drop from $20-30K per implementation to $5-6K based on standards increasing in interoperability.

I am not certain where the original pricing came from, but it is good news since price is one of the major barriers (outside of competitive issues) that stand in the way of establishing interfaces between health applications and systems.

Cost is a driving factor for the use of interoperability standards within clinical intelligence and population decision support initiatives. Recombinant is using components such as continuity of care document (CCD) standards as a method to create analytics capable of separating patients into risk groups using decision support logic.

My hope is that things remain open. As the price to interface through standards diminishes, it should become easier to implement standardized decision support at an individual or population-level, while remaining consistent.

Dan Housman
Managing Director, Analytical Applications

Comparative Effectiveness Research

2009-10-08T08:31:00.005-04:00

Government Health IT magazine recently published “The comparative effectiveness rally”, a great write-up by Phil Carey that explains comparative effectiveness research (CER).

The article includes interesting details from Dr. Brent James, executive director of Intermountain Healthcare’s Institute for Healthcare Delivery Research. He discussed his “Help Two” decision support system implementation as well as the Microsoft Amalga platform.

The Cochrane Collaboration was mentioned as an international, not-for-profit that documents comparative effectiveness. This was the first time I had heard of the organization, and it may prove to be a useful resource. One such example is their report on Antenatal corticosteroids for accelerating fetal lung maturation for women at risk of preterm birth. Unfortunately, the Cochrane library is not a public resource in the United States, but at least it exists and is being maintained through some financial model. From a software standpoint, the next steps for the Cochrane reports should include structured data sets in XML so that the reports can be analyzed and parsed into components for providers and decision support systems. Coding CER data is critical to its availability both at the point of care and when analyzing a population.

Another article worth mentioning in the same issue of Government Health IT magazine is “Medical analytics in war” by Peter Buxbaum. It discusses the importance of integrating population views through GIS systems to monitor outbreaks such as influenza within the theatre of battle.

“The U.S. military’s Medical Situational Awareness in Theater (MSAT) project, a portal application, is being designed to allow users to graphically view potential health threats to troops and to support decision making on the location of military medical units. MSAT will use Web services to allow commanders access to multiple databases and to generate graphical displays that turn raw data into actionable medical intelligence.”

The military frequently relocates troops, therefore it is critical to track location-based views. They are also focused on using Java standards to ensure data is available in component form.

Dan Housman
Managing Director, Analytical Applications

Monitoring pandemics

2009-10-07T14:27:00.002-04:00

The Business Times recently published the article “Technology and the fight against epidemics” by Suganthi Shivkumar. It is quite topical about the use of data warehousing and real-time messaging to monitor pandemics. An example in the article mentions the use of Informatica in Hong Kong to track the SARS epidemic.

With the upcoming H1N1 season fast approaching, it would be helpful if tools such as BioSense from the Centers for Disease Control and Prevention (CDC) had greater adoption. It would also help if local areas effectively leveraged EMRs and related infrastructure to help monitor and contain major pandemic risks.

It is possible that some organizations will launch new data warehouses specifically to fight pandemics; however it is more likely to be an offshoot from existing HIT investments. The financial model is complicated in a world where healthcare networks are independent of each other. That challenge is in the hands of the CDC to figure out.

Dan Housman
Managing Director, Analytical Applications

Peer-to-Peer HIE

2009-10-06T09:22:00.003-04:00

Steve Beller wrote the blog post “A Novel Way to Exchange Patient Health Information”, an interesting take on the NHIN, HIE, and research data warehousing world. It is further evidence of the coming convergence between HIE functions and healthcare data warehousing.

I prefer the decentralized peer-to-peer (P2P) thinking of the proposed solution as well as the simplicity of using Microsoft Office as a platform to share continuity of care document (CCD) messages between physicians. P2P is now infamous from Napster, and is an ideal way to exchange content without central hubs or repositories because it scales quickly and quietly by participants. The general idea of an HIE system involves P2P data exchange, but most architectures of today utilize big hubs.

The Microsoft Office-style exchange may work best for small practices, but not for large integrated health networks. EMR implementations such as EPIC and heterogeneous application systems across hospitals and outpatient facilities require centralized interface engines and CCD factories to consolidate interoperability.

A new twist in the development of a national patient identifier is the use of biometrics. This would avoid reliance on patient reported information which is often inconsistent and the cause of privacy issues. Although I personally like the idea, patient privacy folks may not be pleased with the notion of each office keeping a biometric imprint of their patients with the intention of sharing data.

The thought of universal biometrics reminds me of the movie Gattaca. I find it difficult to imagine every hospital and clinic registration system adding a fingerprint swipe or retinal scan to their hardware and software infrastructure. However, it is a clever idea to address the daunting challenge of uniquely identifying patients amongst a few hundred million people before providing medical facts.

I like the idea of adding de-identified feeds at a patient-level into the mix of the NHIN/HIE/RHIO frameworks for the purpose of public health and research. This is the first time I’ve heard of that idea and it might work for some applications. It may only scale for certain applications, because a warehouse is needed to query complex questions such as cohort size estimations. That being said, ePCRN doesn’t differ much from this approach.

Thanks for the thoughtful posting!

Dan Housman
Managing Director, Analytical Applications

Data Warehousing for Public Health

2009-10-05T13:37:00.003-04:00

I had the pleasure to attend the PHIN conference in Atlanta last month. PHIN refers to the Public Health Informatics Network, a Centers for Disease Control and Prevention (CDC) initiative to improve the exchange of health data.

Recombinant was somewhat of a duck out of water in regards to public health because our focus revolves around clinical research and quality reporting through data warehousing. However, there were quite a few conversations where public health considered themselves uninvited to the table where data was being served.

Our knowledge about i2b2 and the capabilities of clinical systems for data management at hospitals led to some lively discussions and a handful of new opportunities. For example, the CDC has struggled to connect with chronic diseases and i2b2 would be an ideal way to connect to healthcare delivery networks with data management strategies around conditions such as coronary artery disease (CAD), hypertension, diabetes, and chronic obstructive pulmonary disease (COPD).

I met some vendors of interest that intersect with the clinical data warehousing world. A company in the Boston area called Diagnosis One has invested in developing a service including thousands of validated clinical decision support rules. We discussed combining their rule sets with the content that gets extracted and loaded into the Recombinant Data Trust. If anyone is interested in combining both a data warehouse and a privately maintained decision support rule library that is curated by physicians, give me a holler and we will pull together a collaboration with the folks at Diagnosis One!

Another vendor, Trizano, developed an open source public health application that could be a powerful tool linked with a data repository to handle the workflows for public health issues. Given that they focus on the beekeeper model like Pentaho, their licensing model should be compatible with research frameworks such as i2b2. Trizano’s tools might be another key application to drive value out of existing data sets.

The drive for meaningful use has also pushed a lot of interest in HIEs, thus these sorts of tools were well-represented at the conference. I was pleased to encounter the booths focused on IHE-HIE systems. The exhibitors clearly conveyed the message that an HIE doesn't ensure the sort of interoperability that is typically suggested. To ensure one will scale to a national level like an NHIN, the HIE must be implemented to satisfy IHE standards. Among the frustrating and somewhat odd outcomes from the rushed drive toward meaningful use by healthcare systems, is that many HIEs may never be interoperable because even the integration systems have put barriers in front of interoperability.

Based on the PHIN tour of HIE technology, it is now my preference to see more IHE-based HIEs. The Europeans and Canadians are rapidly adopting IHE, but in the United States we haven't wholeheartedly engaged in the standards and efforts at the healthcare-network level. Perhaps it isn't too late for national legislation or state initiatives to include requirements that satisfy international standards.

Dan Housman
Managing Director, Analytical Applications

HIT now Humedica, Inc.

2009-09-28T12:30:00.003-04:00

Health Insight Technologies recently announced that it has rebranded as Humedica, Inc., a venture-backed firm in Boston that plans to offer a software-as-a-service approach to clinical intelligence. Their model intends to reduce the burden of BI implementations by eliminating local infrastructure. Using ETL services, Humedica will store healthcare data at a national-level using a centralized clinical data warehouse; then offer access to the data for quality reporting and research, presumably for a fee. The article “Humedica Wants to Dose U.S. Healthcare Crisis with Clinical Analytics” by Ryan McBride offers a great overview of Humedica’s impressive endeavor.

Dan Housman
Managing Director, Analytical Applications

Presentation at Oracle OpenWorld

2009-09-25T16:25:00.001-04:00

Recombinant is scheduled to co-present with Amazon Web Services at the upcoming Oracle OpenWorld conference on October 14th in San Francisco, CA. Joseph Adler, Solutions Architect will explore the cost, performance, and operational advantages of healthcare and life sciences data warehousing in the cloud. For more information about the conference, visit the OpenWorld website.

Genetic studies through data warehousing

2009-09-23T08:22:00.002-04:00

The i2b2 and related Crimson team recently published an article titled "Instrumenting the health care enterprise for discovery research in the genomic era". The article describes the potential to acquire large-scale samples needed for genetic studies by integrating data warehousing with lab systems.

Hospital network infrastructure for phenotyping and collecting biospecimens for biorepositories and omics data acquisition has so much more scale than current methods. New ideas and pioneering work in this area are likely to make a substantial impact in how translational research evolves over the next few years.

Recombinant expects to see both consented and non-consented/discarded sample models for high-scale phenotype-genotype matching. Ideally these models will integrate with translational research stacks such as i2b2, caBIG (caTissue/caGRID), and frameworks like GenePattern for analytics.

Recombinant is working on multiple projects to bring some of these ideas into an open source tool set that can be implemented across sites via the same model as i2b2.

Dan Housman
Managing Director, Analytical Applications

BIDMC funding decision support

2009-09-22T08:26:00.003-04:00

John Halamka published a blog post titled “The Draft FY10 IS Clinical Systems Plan” regarding Beth Israel Deaconess Medical Center last week. CIOs at health systems always have a long list of projects and a finite amount of resources. It was sobering to read that BIDMC won’t have a big bull’s eye on data warehousing, however there was funding for decision support in the plan for the following areas:

Implement Performance Manager reports and dashboards as needed to support
organizational needs.
Implement clinical data marts as needed to enable quality measurement, pay
for performance goals, and other decision support needs.
Enhance the Community Provider Index to better support Health Information
Exchange via NEHEN gateways.
Implement enhancements to the Patient Activity Profile to support enhanced
reviews required by JCAHO.
Enhance SOAR (Accounts Receivable workflow) to support denial tracking and
appeals workflow
Explore the introduction of new Business Intelligence tools as funding
permits
Support Cactus and NEHEN Express users

Dan Housman
Managing Director, Analytical Applications

Understanding the i2b2 ontology

2009-09-21T16:48:00.002-04:00

Marcia Gulesian wrote a blog post titled “Functional Design of an Ontology” which provides a helpful introduction and analysis of i2b2.

Dan Housman
Managing Director, Analytical Applications

Roles on a data warehouse team

2009-09-16T16:19:00.004-04:00

There is an interesting blog post about healthcare data warehousing titled “What do copy editors and data miners have in common?” by Blake Zenger. His comparison of data miners and warehouse developers to the relationship between editors and authors can help folks improve their understanding of the roles on a clinical data warehouse team.

Dan Housman
Managing Director, Analytical Applications

The challenge of non-events

2009-09-15T17:15:00.001-04:00

It is relatively easy to analyze healthcare data for activities that have occurred, but difficult to confirm when an activity has not. If a patient is diagnosed with a disease, we know about it because it is indicated in their medical record. However, a medical record without a diagnosis does not guarantee that the patient is free of the disease. Many of the activities that are important to researchers relate to things that didn’t occur, or are at least closely related to the “dark matter” in the data. A vast amount of interesting information is hidden in the murky area of missing data.

A recent example involved the analysis of a population of diabetes patients. We segmented the data set into periods to identify patients who either performed or did not perform an HbA1c test every six months. The algorithm identified a surprisingly large population of non-performers from the 2008 data; approximately three times larger than expected. The query attempted to pull all of the patients in the database who met the criteria for our definition of diabetes. This definition, for simplicity sake, meant that the patient was diagnosed at some point and was currently alive. This definition was flawed; just having data about a diagnosis did not guarantee that we also had data about the non-event of missing an HbA1c test within the last six months.

The first problem we identified was a substantial number of patients from the population of about 100,000 stopped receiving care in the health system after they were diagnosed within a 15-year period of data. This meant that the non-event of skipping an HbA1c required validation against a second non-event of not receiving any care at all. It was relatively easy to resolve this problem by eliminating patients from the query who had no facts in the data warehouse for the previous five years. Patients with a chronic disease should have had healthcare visits within this time frame, however the decision was arbitrary. Had we instead chosen to ignore patients without a visit during just one previous year, our results might have been suspicious.

One consideration was to analyze enrollment data from insurance payer files, however it wasn’t readily accessible from the EMR, and the data was incomplete given that it omitted patients without insurance coverage from major commercial payers. The insurance payer files would have had PCP data, but likely nothing about patients who stopped receiving care from one health system while having a PCP in another.

There was also a significant challenge to capture the patients that would come in and out of the system only when they were extremely sick. Those patients deemed themselves to be healthy enough to avoid having a doctor’s visit or a lab test for a couple of years, but then suddenly showed up for one and then disappeared.

Another non-event occurred among patients who frequently relocated. There was a field in the data warehouse to track each address a patient had in their history, however it came from EMPI registration data, and most patients did not update their registration, especially when departing the system.

Despite having a field for vital status in their records, we could barely verify with certainty whether a patient was alive or not. While a morbid thought--at what visit to the physician was a patient to report if they had passed? Death was only consistently verifiable when it occurred in a hospital or if information was received from social security records. Inevitably there were families with a financial motive to conceal mortality because social security checks arrived by mail for living elderly citizens with limited assets.

Regardless of the challenges, these were the patients that needed to be identified for important testing and monitoring of their chronic disease state. How were we to know who was a patient when they were not consistently in the health system as facts? We had to identify patterns among both the visits that occurred as well as the non-events. These were linked pieces of information operating in opposite worlds. A lack of visits could be dispelled by a visit. The patterns were not universal for interpreting quality. A patient with a certain age, gender, health, race, and economic background could be expected to have a very different level of activity in the system, thus a universal rule couldn’t apply to all patients and all measures of performance.

The nature of clinical intelligence work often deals with unknown non-events that are never registered as facts in data sets. This creates doubt about the quality of the analyses and recommendations.

This is just a few of many reasons for chart abstraction in core measures. When structured medical record fields are missing event data to comply with a measure, it doesn’t guarantee that the event never occurred nor does it eliminate the possibility of it being documented in a clinical note. The only way to eliminate doubt from a small set of cases is to analyze the full chart of each patient according to CMS guidelines.

I encounter instances of non-events on a daily basis. In many cases, it is a factor of whether or not we have up-to-date data. It can often take up to 30 days to receive information from a source system through a monthly load. Many source systems aren’t capable of providing instant data feeds for analysis or it is too cost prohibitive to do so. This requires us to run reports against information that is near real-time, meaning it is not up-to-the-minute but relevant for the state. An example would be to determine whether or not a patient received a lab test that was ordered two months ago. We are unable to provide 100% confidence for non-events without up-to-the-minute data. However, if we have a record of a test being performed, we are 100% certain that it occurred. In many instances, we need to account for latency in the reporting and provide tolerances for the lack of information. Grace periods are required to account for these uncertainties.

While it is challenging enough to access the data we have, it is critical to think carefully about the data we don’t, and the impact it has on our results. Maybe we need more data? I certainly find it odd that in a world where many people tweet ten times a day, we have tremendous uncertainty about individual health.

Dan Housman
Managing Director, Analytical Applications

Day Two from the HDWA Conference

2009-09-14T16:13:00.001-04:00

Dr. Charles M. Watts, senior vice president of medical affairs at Northwestern Memorial Foundation, presented an overview of Karl Weick’s high-reliability organization principles in relation to healthcare. Among his key points was an importance to focus on failure in order to ensure safety. He stated that "Chronic wariness is the tone in a safe environment...hubris is the enemy."

Dr. Watts simplified data warehouse quality as "data quality equals completeness multiplied by validity." He provided an example of an average newborn baby weighing 32 kilograms or about 71 lbs in a healthcare system. The occurrence was attributed to inconsistent data entry with staff using kilograms and grams interchangeably. The solution was to transform the data into one unit of measurement and to ultimately correct the consistency in data entry.

Dr. Watts also demonstrated two cases of applying improvement to increase safety and reliability. The first case involved shoulder dystocia, a tremendous liability risk that occurs when a newborn is stuck in the birth canal. By instituting a simulator, a standard protocol, and a training program, the existing $20 million annual liability was successfully eliminated. The second case involved a decrease in severe adverse events even though the total number of reported adverse events actually increased. This was attributed to improvements in both safety and visibility. Dr. Watts stated "I don't think mistakes went up--reporting of mistakes became more acceptable and we should celebrate that."

Deb Batson, clinical research data warehouse architect at Children’s Hospital Denver, mentioned an example of finding married 6-year-olds. This mistake was attributed to a registration system that was prone to data entry error during hospital admissions.

The reports from Memorial Sloan-Kettering, National Institutes of Mental Health, Duke, and Ottawa differed tremendously. It would be helpful to find a better way to execute technology transfer of reports between organizations.

The folks at Intermountain built an amazing tool for improving labor costs using Cognos as well as a meta-report search engine. The engine allowed users to browse and launch reports from multiple BI tools using just one portal. It appeared to be a good solution for groups with more than one reporting tool.

Dan Housman
Managing Director, Analytical Applications

Update from the HDWA conference

2009-09-10T12:50:00.003-04:00

There were a handful of interesting presentations from Northwestern Medical Faculty Foundation (NMFF), Ottawa Hospital, Duke, and MD Anderson on the first day of the HDWA conference.

NMFF presented an open source SQL server integration services extension for regular expression extractions from free text, a de-identification utility, and TaskMaster, a system for processing ad-hoc data requests. One of the clever features in their workflow management approach was an integration that pulls data from the eIRB into the data warehouse to display the details of the eIRB process. It also had the capability to link out from the tool to execute tasks such as creating a report from a SQL query. Another interesting component of their model was the use of distributed analysts to query the database. The analysts operated within their own groups, but this required segmented hospital data to prevent inappropriate queries.

The Ottawa Hospital presented a dashboard view of their hospital-acquired infections graphically overlaid onto the hospital floor plan to identify infection hotspots.

Duke presented a poster on an open source framework that supports patient recruitment for clinical trials using Mirth.

MD Anderson created a new group called the Office of Performance Improvement. The organization utilized Minitab, QI Analyst, and ultimately Statit to effectively generate control charts in order to rectify challenges found among common BI tools such as Cognos and Business Objects. One of the challenges was an inflated length of stay measurement from last year. This was due to Hurricane Ike, as it was inappropriate to discharge patients in the midst of 110 MPH winds.

One of the common trends among the HDWA presentations was an initial model for data warehouses and delivery systems that provided free access in order to drive adoption, but eventually transitioned to a fee-for-service model for sustainability. Only a few of the organizations were successful thus far in making that transition.

Dan Housman
Managing Director, Analytical Applications

Consumer behavior in healthcare

2009-09-09T13:05:00.004-04:00

Imagine for a moment that the government issued a mandate that required consumers to pay a significant portion (75% or higher) of all healthcare costs out-of-pocket.

This is a serious reality for many tourists who arrive in the United States from foreign countries without insurance, but with a reasonable bank account, and a need for care.

When this occurs, a provider must not only explain the options available, but also the cost of each to the patient. Procedures are then chosen based on a balance between cost and a desire for the highest quality outcome.

Among the challenges providers struggle with is how to explain the cost of a procedure in marketing materials or during a consultation visit. This isn't quite as simple as one might think, e.g. determining the cost of a colonoscopy. The total cost to the patient has a real chance of including the removal of polyps and a need for a secondary colonoscopy. The patient visit also includes underlying services beyond the core procedure; and the cost of these additional activities must be accounted for.

This situation warrants the need for procedure pricing that is averaged for high and low costs based upon potential episodes. Packages need to be established with pricing and options clearly communicated to allow for consumer choice. A handful of health systems are now attempting to cater to this market using simple analytics to support pricing models.

In contrast, the decision-making of an insured patient differs tremendously. They either do not worry about the cost of a procedure or they choose the opposite of the lowest cost. And why not? If a patient is fully covered for a colonoscopy with the option of either a $3,000 or an $8,000 procedure, they will likely choose the more expensive option under the assumption that it is superior care.

At the moment I am scheduling my own dental care for 4 crowns to minimize out-of-pocket expenses. My dental insurance plan has a maximum annual coverage limit, thus I make medical decisions based upon what I must pay. It appears that dental insurance is more efficient than health insurance in this way. It encourages consumer behavior, but generally covers maintenance and prevention activities.

Now imagine that competition on price became a leading healthcare issue, created by consumers forced into out-of-pocket buying decisions. Perhaps a price and quality comparison website would appear in the market. Prospective patients would select from a list of procedures and then nearby providers would list their pricing. Some providers would likely advertise to get to the top of the list, while others would simply rise to the top through competition. Patients might even rate or comment on the outcome of a previous procedure using such a tool.

Medicine at times has taken a track opposite to the one governed by Moore’s law in the semiconductor industry. The evolution of new technologies in medicine serves to increase cost. No one that I am familiar with is working on the assumption that we will reduce the cost of colonoscopies by 50% every 5 years.

How do we encourage patients to act as consumers? Consumers are needed to improve quality and minimize costs.

One place to start is to evaluate quality through patient reported outcomes and let the mix between insurers and providers figure out how it drives the market. Perhaps insurers should reward the healthy with financial incentives.

It appears that capitalism has failed, but I don't know why.

I’ve read a handful of research studies that mentioned inactive patients were uncomfortable with consumer-driven insurance spending because it required effort on the patients’ behalf to reap the benefits. This suggests that such a plan would encourage patients to work toward becoming healthy. Could these plans become more cost-effective for employers over time? Will self-insured employers or HMOs--folks who bear the full cost of the risk do some pioneering in this area?

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Outbreaks near me

2009-09-03T10:39:00.006-04:00

I’m not an iPhone user, but I’d like to be one today. HealthMap, an application created by Clark Freifeld and John Brownstein from the Children’s Hospital Informatics Program, uses GPS to determine when a disease outbreak occurs near an iPhone user; and thus alerts them of the danger.

I’d like to see HealthMap ported to BlackBerry devices- that way I too could avoid the next dinner party in a suburb with a major H1N1 outbreak, or drive around an Ebola or Marburg traffic jam!

HealthMap is a free download from the iTunes store.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Launching Bell Labs of Health Care

2009-08-28T12:45:00.001-04:00

Patrick Soon-Shiong of APP Pharmaceuticals donated $1 billion to create Bell Labs of Health Care according to the article “Billionaire Patrick Soon-Shiong On Health Care, Obama” by Kym McNicholas from Forbes. The philanthropic investment is intended to create a public health grid.

“The idea is to actually go across the country and bring scientists, mathematicians, computer scientists, engineers, biologists, clinicians, surgeons, oncologists, pathologists, all together. And really integrate, truly integrate, information from the basic science to the bench to the clinic” said Patrick Soon-Shiong.

At Recombinant, we look forward to this effort taking shape and hope to get involved in the project.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Clinical trials model for BI within OBIEE

2009-08-28T11:54:00.000-04:00

A clinical trials model was referenced in the blog post “OBIEE for Clinical Trial Management System (CTMS)” by Ramana Chittor. It focused on the tracking of trial activity and less about the clinical observation components of the CTMS. This is an interesting high-level trial and business model with mapping in OBIEE.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Healthcare costs slow industry growth

2009-08-28T11:37:00.001-04:00

There is an interesting blog post titled “Industries Hurt Most by Soaring Health Costs” by Rick Newman from U.S. News and World Report. The post suggests that healthcare efficiency is linked to overall economic growth. Industries that traditionally provide their employees with company-sponsored health insurance are the ones experiencing the slowest growth. While I am not familiar with the full extent of the details, it is clear that the cost of healthcare is a burden for every industry.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Close encounters with DOS and SCO

2009-08-26T09:25:00.003-04:00

Many fail to realize that the state of healthcare IT is firmly entrenched in legacy technology. I’ve encountered this trend in recent travels to healthcare organizations that would rather maintain an old technology until it fails than delve into the effort, expense, and potential risk of implementing a new solution to replace it.

One recent encounter involved a group of CIOs evaluating the purchase of an ESB (Enterprise Service Bus – used for HL7 messaging) for research collaboration. The CIOs questioned why the ESB vendor had only a few customers upgraded to the latest platform. The vendor deferred the question back to one of the CIOs who owned the eight-year-old legacy application; why haven’t you switched to the new platform? The CIO stated that the old application still worked and the upgrade provided a greater potential risk than reward. Despite all of the limitations in features, flexibility, and age of the legacy system, the upgrade required too much of an engineering effort. Simply speaking, the response reflected the phrase: if it ain’t broke, don’t fix it!

A second encounter occurred at a small practice involved in a PQRI initiative to code CPTII codes in billing data. Each practice involved in the initiative had to transmit their billing data to a central quality reporting repository for analysis. I was called in to this particular practice for my knowledge of an ancient technology – DOS! The practice managed billing on an EPM (Electronic Practice Management) application using the same back room white box server since 1989. The machine in question utilized a DOS prompt to access billing data. After a bit of tweaking, we extracted the data to a floppy disk and onto a laptop through a USB floppy drive. At a subsequent meeting, the youngest person in the room asked, what is a floppy drive? The user of the machine, who dutifully processed billing for the practice, remarked that should the system ever completely fail, it would be time for them to retire. That would be an unlikely scenario due to the EPM’s built-in 8-track backup system.

While the DOS box from 1989 wins the record for oldest system I’ve encountered, we have seen plenty of SCO servers from the mid-1990s as well as a host of old platforms operating in the corners and back rooms of physician practices. At this point, I wouldn’t be surprised to find an Atari 2600 running a lab order tracking system or an 8086 storing vital signs for over a million patients.

Nothing legacy is shocking anymore.

As we progress toward the powerful future of PHRs, HIEs, NHINs, and EDWs, DOS is just one example of a legacy system that must communicate with modern applications. There are thousands of DOS machines still spinning disk drives that were built before the college graduates of today were born. Many of these machines serve as the primary source of electronic data that we need to share and analyze. There is a last mile. And healthcare applications don’t die without a fight.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.

Bugs

2009-08-14T15:48:00.006-04:00

Like all software companies, we spend a fair share of our time trying to resolve bugs. Many people hear the word bug and it sends shivers up their spine. They envision some beast that eats all of their data never to return it again. Bugs are an acquired taste and for us we actually enjoy a good bug once we can track it down and fix it.

With a couple of major releases in July we hit some interesting bugs. By describing them in detail I think the process of debugging can be an opportunity to play Sherlock Holmes and then achieve that satisfying epiphany in an ah-ha moment. I do wish someone would create a museum or book of bug patterns. Maybe someone has. But a layperson's guide including the debugging process would be ideal.

Case 1: The case of the over green heat map

Biologists like to see data in heat maps. It feels natural for them to try to view the complexity of genes and samples with everything lined up in a colorful grid. We built a tool to view a grid of gene expression data. It was looking fine but we decided to modify our normalization routine to improve the accuracy of the results. Normalization is needed for heat maps because taking numbers from zero to N makes it difficult to set a scale for what should be red and what should be green. In a normal distribution using a bell curve, items that are in the extremes are bright colors and the ones in the middle are lighter versions of the same.

A problem was reported to us; our heat map was showing data that was too bright green, meaning the data wasn’t represented accurately. This caused some stir given that we had just improved it. So, we went to work trying to identify the cause. The first hypothesis suggested outliers. We proposed samples that were 3 standard deviations could be throwing off the whole scale of the heat map.

We took numbers greater than + or - 3 and set them to 3. This seemed to help but things were still too green. We then reviewed the data directly and it looked normal. However one thing was odd; the extremely small numbers were bright green. This led us back to the question of scales and outliers and reexamining our groupings of samples.

One engineer eventually cracked the bug. The small numbers were bright because they weren't recognized in the heat map as small. But why did this occur in the new heat map version? The small near-zero numbers were being read in scientific notation, e.g., .0000003 can also be 3 EXP-6. The heat map recognized it as 3. It changed because the new algorithm was more precise and was carrying more information in it through additional significant digits. More significant digits meant conversion into scientific notation more often.

We modified the database procedures to remove the unnecessary digits and verified the data wasn't presented through the code in the heat map using scientific notation.

Case 2: The mysterious failed report migration

Due to schema changes to a data mart, a number of Crystal reports were modified to encode logical updates to reflect the modified schema. Each were built in a development environment and all successfully tested against the development database.

We then needed to move the reports to QA. Upon migration of the database and schemas, the reports failed in the report server. This was quite odd; the reports worked in development when pointing the report server to the QA database, but failed in QA. We meticulously checked the configuration files and couldn't find a difference. However, there was a pattern in report failures, just the ones we had modified.

Upon verifying the files were the same, I came to the following conclusion: something was different in development vs. QA on the servers and something was different between modified vs. not modified reports.

Given that there was a big clue (error message "could not log on to database"), I compared both the report types modified vs. unmodified for the SQL connection properties. Everything was identical except for one thing. The modified report connection type had switched from SQL SERVER OLEDB to the SQL SERVER NATIVE driver. Upon switching it back in each report to OLEDB, the problem was resolved in QA.

Why? Development is a curious environment vs. QA. In QA, we keep the database and application server separate. In development, both are on the same machine. This meant that the SQL SERVER NATIVE driver was installed with SQL SERVER and was the difference in why it worked in development but not in QA. Case closed.

Dan Housman
Managing Director, Analytical Applications
Recombinant Data Corp.