A key challenge to making full use of your Patient Satisfaction Survey Data is learning how to integrate it with your EHR data. Having an understanding of a patient’s associated encounters can provide valuable context on the results you receive on a Patient Satisfaction Survey. The challenge oftentimes is having the right data to link these together.

Here are 5 strategies to enable integration and fully leverage EHR and Patient Satisfaction Survey Data.

1. Use MDM to match patient information
   • Use the patient submitted information to match the patient record in the EHR
2. Ensure pass-through of encounter information to your survey provider
   • If the encounter is captured as part of the Patient Survey, then ensure that you receive this your survey provider for integration with clinical records
3. Create a complete patient record from the data warehouse
   • This aggregates the encounter(s) relevant information for the patient to provide context to the survey responses
4. Group surveys by service location and practitioner
   • Using the integrated EHR data, then group survey results by location and practitioner to determine trends
5. Use integrated data to drive change
   • Identify leading indicators to unfavorable patient satisfaction scores and then use these to drive change in the organization

Using patient survey results in conjunction with EHR records can remove operational blind spots that can exist when using Patient Survey Data solely. When fully integrated, you can move on to identify trends. This then should be used to promote change and improve patient experience and well-being.

I just got back from the doctor whose office is in one of the leading hospitals in the United States. I was his first appointment of the day.

“They just updated Epic this morning, and everyone is getting into the office trying to make sense of the changes,” the doctor said. I asked if he knew that changes were coming. He said someone came around several weeks ago and told employees that a new release will be installed and that there would be someone available to answer questions from employees…at some point.

The doctor seemed a bit frustrated because the screens and process he’s been so used to have changed. At first glance, he said putting in orders for patients takes several more steps. The user experience has changed for doctors and other hospital employees. You could hear the chatter around the office, and everyone seemed annoyed.

The doctor said he just needs to sit down and figure out what changed. And, he’s right. He needs to take some time and explore the new version.

But as I was talking to him, all I could think about was organizational change management. If they had a good change management plan, it would have likely eliminated the frustration and allow employees to focus on their patients.

The four components to a good change management plan:

1. Define the change: Explain the change and why it’s important
2. Communicate the change: Tell the “what, when, how, and why” to the affected groups
3. Enable the change: Make sure employees have the training and support required
4. Sustain the change: Develop and share best practices, and adjust where necessary

Changes to EHR systems are inevitable. However, you need to plan accordingly. Time and time again, we see situations in which change management could help improve the user experience, increase adoption, and improve ROI. It’s time to take change management seriously and make sure it’s part of all your IT projects.

Let us know how our change management and healthcare teams can help you.

Researchers with the U.S. Army claim that clinical data visualization is a key component in the usage and delivery of EHRs because it:

• Presents data in a pleasing, easy-to-understand manner
• Makes digesting and sharing data faster and simpler
• Reduces the time needed to interpret and understand data points
• Increases the user’s ability to see trends and patterns in patient data

Data analytics and visualization tools also allow for the smooth communication between practitioners and patients. With the assistance of communicative charts and interactive dashboards, users can absorb information more efficiently and use it more effectively.

The design and format of the information presented are crucial factors that should be taken into consideration when producing visual aids to explain the significance of the data collected.

Ultimately, analytics solutions allow healthcare practitioners to harness a better understanding of patients’ health, the available treatments, and the expected procedure outcomes. They also reduce the time spent on managing data and instead increases time spent putting the data to work for organizations and their patients.

The financial benefits of analytics solutions cannot be underestimated.

We recently published a guide that explores how data and technology can enable organizations to make informed healthcare decisions, produce better patient outcomes, and create a better patient and stakeholder experience. You can download it below.

This blog was co-authored by Tom Lennon.

Healthcare IT is ever-changing and Perficient is on the forefront of this change, guiding the industry and those we serve toward a brighter future. We partner with healthcare companies to help people live their lives to their fullest potential today, using best practices and cost saving technologies and processes.

As we look to the future of Healthcare Information Systems, the effectiveness of an organization is measured by four areas; the heart of who we are and do is all about the integration, accuracy, consistency and timeliness of health information.

Healthcare organizations are among the most complex forms of human organization ever attempted to be managed, making transformation a daunting task. Despite the challenges associated with change, organizations need to evolve into a data-driven outcomes improvement organization.

They aggregate tremendous amounts of data – they need to figure out how to use it to drive innovation, boost the quality of care outcomes, and cut costs.

Data Integration Challenges

Besides members and providers, as well as internal/external business partners and vendors, there are a multitude state and federal regulatory/compliance agencies that insist on having our information on a near real-time manner in order to perform their own functions and services.  These integration requirements needs are constantly changing.

As an EDI Integration Specialist, I have seen many organizations struggle to constantly keep up with the business needs of their trading partners, state and federal agencies. Often, as our trading partners analyze the information we have sent them, they discover missing data or inconsistencies.

This requires a tedious and painful iterative remediation process to get the missing data, and results in resending massive amounts of historical data or correcting/retro-adjudicating claims.  Adjusting and recouping claim payments is always painful for all entities involved, especially providers, with possible penalties or sanctions.

In the last few years, I have worked with several clients on getting their claims information loaded into their state’s All Payer Claims Databases (APCDB) and CMS to get their health claims reimbursed. We struggled to get the complete data set loaded successfully, and to meet the rigorous quality assurance standards.

It required several attempts working with their legacy systems to get the necessary data into the correct format. It required a great deal of coordination, testing and validation. Each state has a different submission format and data requirements, not necessarily an 837 EDI format, including one state that had a 220+ field delimited record format (Rhode Island).

We spent a great amount of time in compliance validation, and each submission required a manual effort. We constantly had to monitor each submission’s file acceptance status, handling original and adjusted claims differently using the previously accepted claim ID. If files were not submitted accurately and on a timely manner, there were significant fines imposed.

Several times we discovered that even though the files were successfully accepted, there were still missing information which need to be resubmitted. To be honest, it was a logistical nightmare.

As we design and develop data integrations, APIs and extracts, we often ‘shortcut’ to deliver data due to competing priorities, quickened project delivery schedules or limited development/testing staff. This leads to not giving our full attention to the complete requirements of the client/trading partners.

Companion guides and documentation are vague and say ‘send if known’, realizing several years later that these ‘shortcuts’ will be found out and possibly leading to penalties and corrective action plans. Sometimes legacy system and technical limitations lead to not having the complete record set that is required.

Limitations of electronic health record (EHR) system combined with variable levels of expertise in outcomes improvement impede the health system’s ability to transform.

In many healthcare organizations, information technology (IT) teams—including data architects and data analysts—and quality and clinical teams work in silos. IT provides the technologies, designs and delivers reports, without a clear understanding of the needs of the quality and clinical teams.

This can sometimes turn into a finger pointing exercise. Quality and clinical teams claim IT is not delivering the data they need to succeed, while IT insists that others are not clearly articulating what they need. It takes clear-eyed analysis to see that the teams are failing to work together to prioritize their outcomes improvement initiatives and drive sustainable outcomes.

How Can Health Care/System Redesign Be Put Into Action?

At Perficient, we can provide a comprehensive picture of your organization’s information needs and provide you with a path to implementing complex system redesigns and simplify integrations.  Putting health care redesign into action can be done in the following four general phases:

1. Getting started.  The most important part of building a skyscraper is looking at the requirements, developing a blueprint and building a robust foundation.  The first phase involves devising a strategic plan and assembling a leadership team to focus on quality improvement efforts. The team should include senior leaders, clinical champions (clinicians who promote the redesign), and administrative leaders.  We need to develop a long-term strategy that sunsets legacy systems, consolidates business functions, build synergies between departments and aggregates data into a central repository.  High-level needs assessments are performed, scope is defined to limit effort, and a change management process is created to assist in project management.   A business governance committee determines what and when business decisions are implemented.  Technical/architectural review committee approves the overall design and data governance of systems, interfaces and integrations of enterprise systems.

2. Review the complete electronic dataset.  That includes building a corporate data dictionary (including pricing/benefits, membership, providers, claims, utilization, brokers, authorizations/referrals, reference data and code sets, etc.) and set priorities for improvement. The second phase involves gathering data to help inform the priorities for improvement. Once data requirements are gathered, performance measures such as NCQA/HEDIS that represent the major clinical, business, satisfaction, and operations goals for the practice can be identified.   Corporate reporting and process needs are critical at this phase to look to ensure compliance and meeting internal and external customers’ requirements.  The creation of dashboards and user reports that are easy to manage provide the right information at the right time can make the difference of cost savings and effective management throughout the organization.  Using these dashboards allow users to keep an eye on the overall health and utilization of the services that they provide to their members.

One of the most helpful EDI integration practices I have found is to perform a source to target gap analysis between core claims/membership systems, my inbound/outbound EDI staging database, and the EDIFEC/GENTRAN mapping logic which translates the data to the outbound and from the inbound x12 EDI 837 Claims and 834 Membership enrollment files.  This document also identifies any transformations, conversions or lookups that are needed from propriety values to HIPAA Standard values.  By looking at every EDI Loop/Segment/Element and mapping it all the way through, I was able to identity data fields that were not being sent or being sent incorrectly.   I give this mapping document as part of my technical specification documents to my EDI developers, which I customize for specific trading partners while I was reviewing the vendor’s companion guides.

3. Redesign care and business systems. The third phase involves organizing the care team around their roles, responsibilities, and workflows. The care team offers ideas for improvement and evaluates the effects of changes made.   Determining how an enterprise integrates and uses often disparate systems is critical to determine timely, complete and accurate data/process flow.  The design, creation and use of APIs and messaging technologies assist in getting information extracted, transformed and loaded (ETL) is critical, especially if information is to be used real-time web-based portals.  Evaluation of easy to use yet robust batch process ETL tools, such as Informatica, become the cornerstone of any data integration project.   Healthcare organization relay upon reporting tools to evaluate, investigate and reconcile information, especially with their financial and clinical systems.   Imaging, workflow management and correspondence generation systems are used to create and manage the communications.

4. Continuously improve performance and maintain changes. The fourth phase includes ongoing review of clinical and financial integration outcomes and making adjustments for continued improvement.  As we are looking to the future, we need to look at the IT architecture and its ability to expand with the ever-changing technology and needed capability models.  Perficient is a preferred partner with IBM, Oracle and Microsoft with extensive experience for digital and cloud based implementations.   Using these technologies gives our clients the ability to expand their systems,  application servers to be spun up on demand based on need and growth, allow for failover, allow for redundancy, distributed and global databases to be employed, virtualization of software and upgrades be made while being transparent to the end users.

Perficient’s health information technology (IT) initiative for the integration of health information technology (IT) and care management includes a variety of electronic methods that are used to manage information about people’s health and health care, for both individual patients and groups of patients. The use of health IT can improve the quality of care, even as it makes health care more cost-effective.

Bringing in an Analytics/Reporting Platform

Implementing an enterprise data warehouse (EDW) or a data lake/analytic platform (DLAP) results in the standardization of terminology and measures across the organization and provides the ability to easily visualize performance. These critical steps allow for the collection and analysis of information organization-wide.

The EDW/DLAP aggregates data from a wide variety of sources, including clinical, financial, supply chain, patient satisfaction, and other operational data sources (ODS) and data marts.

It provides broad access to data across platforms, including the CEO and other operational leaders, department heads, clinicians, and front line leaders. When faced with a problem or question that requires information, clinicians and leaders don’t have to request a report and wait days or weeks for data analysts to build it.

The analytics platform provides clinicians and leaders the ability to visualize data in near-real time, and to explore the problem and population of interest. This direct access increases the speed and scale with which we achieve improvement. Obtaining data required to understand current performance no longer takes weeks or even months.

Application simplification takes the confusion as to the consistency and the accuracy of data within an organization. Per member/Per Month (PMPM) reporting is delivered in a standard format throughout, regardless of line of business.

The analytics platform delivers performance data used to inform organizational and clinician decision-making, evaluate the effectiveness of performance improvement initiatives, and increasingly, predict which patients are at greatest risk for an adverse outcome, enabling clinicians to mobilize resources around the patient to prevent this occurrence.

An analytics platform is incredibly powerful and provides employees and customers with the ability to easily visualize its performance, setting the stage for data-driven outcomes improvement.  However, healthcare providers and payers know that tools and technology alone don’t lead to improvement.

To be effective, clinicians, IT, and Quality Assurance have to partner together to identify best practices and design systems to adopt them by building the practices into everyday workflows. Picking the right reporting and analytical tool and platform is critical to the success of the integration project.

Big data tools such Hadoop/HIVE/HUE and cloud technologies are used to bring together various data source together into a unified platform for the end-user.

Roadmap to Transformation

Perficient provides a full service IT roadmap to transform your healthcare organization and achieve both an increased personalization of care via the same path: digital transformation in healthcare. New health system technology, such as moving beyond basic EMR (Electronic Medical Record) infrastructure to full patient-focused CRM (Customer Relationship Management) solutions, has enabled healthcare organizations to integrate extended care teams, enhance patient satisfaction and improve the efficiency of care.

We connect human insight with digital capabilities in order to transform the consumer experience and deliver significant business value.

For more information on how Perficient can help you with your Healthcare IT integration and analytical needs, please see https://www.perficient.com/industries/healthcare/strategy-and-advisory-service

Exposing patient health information as Application Program Interfaces (APIs) is one of the most critical components in Stage 3 of the EHR Incentive Programs and all providers will be required to comply with MU3 requirements by 2018. The APIs will ensure improved patient engagement by providing data access in application of patient’s choice instead of current patient portal channel only.

In compliance with HIPAA privacy and security rules, the implementation of APIs that expose sensitive PII/PHI data must be properly protected in terms of Authentication, Authorization, Audit, Message and Transport Level Security, Encryption etc.

Some of the key requirements for the API interface are:

1. Uniquely identify a patient and return an ID or other token that can be used by an application to subsequently execute requests for that patient’s data.
2. Allow a request for “all” the patient data, or specific “by specific data category” i.e. demographics, labs, procedure, medications etc.
3. Must include accompanying documentation that contains API syntax, function names, mandatory and optional parameters, methods and their returns, and terms of use.
4. Documentation must be available via publicly accessible hyperlink
5. Authentication, Access Control, Authorization , Auditable Events and Tamper-Resistance — Trusted connection — Auditing actions on health information

Solution using IBM APIC:

1. APIC security policies jwt-validate or validate-usernametoken can uniquely identify a patient and use of JWT/OAuth token management system through DataPower can return token to requesting client for subsequent requests.  
2. APIC is an integrated creation, runtime, management and security foundation for enterprise grade API’s, and Microservices to power modern digital applications. Fine-grained services based on data category demographics, lab results etc. can be quickly built with standards-based visual API spec creation in Swagger 2.0.
3. APIC provides customizable, self-service developer portal for publishing APIs. Through the portal, application developers can access APIs directly and can take sample code snippet from the portal to implement APIs in their applications.
4. APIC self-service developer portal can be accessed publicly for API subscription and documentation.
5. IBM APIC provides access control over API’s, API Plans and API Products. API runtime environment – API gateway (Micro gateway or Datapower) acts as Policy Enforcement Point, and maintains security and control to APIs. Datapower’s AAA policy can support variety of Authentication/Authorization formats JWT, LTPA, SAML,WS-Security, OAuth etc. Datapower should also be used to provide message level security by encrypt/sign and transport through the use of SSL/TLS.

We’ve talked a lot in this blog about the use of electronic health records (EHRs) in clinical trials. Recently, my colleague wrote about it on our healthcare blog, so, I thought I would share her post with my pharma and CRO friends. While our previous posts focused on the use of EHR for subject recruitment and site/study feasibility, my colleague’s post is specifically about using the data as part of the research itself.

The FDA just released a new draft guidance document called Use of Electronic Health Record Data in Clinical Investigations. The document provides readers with recommendations on:

• Deciding whether and how to use EHRs as a source of data in clinical investigations
• Using EHRs that are interoperable with electronic systems supporting clinical investigations
• Ensuring the quality and the integrity of EHR data that are collected and used as electronic source data in clinical investigations
• Ensuring that the use of EHR data collected and used as electronic source data in clinical investigations meets FDA’s inspection, recordkeeping, and record retention requirements

The purpose of the document is to:

• Facilitate the use of EHR data in clinical investigations
• Promote the interoperability of EHRs and electronic systems supporting the clinical investigation

The draft guidance focuses heavily on interoperability: “the ability of two or more systems or components to exchange information and to use the information that has been exchanged.” Integrations between EHR systems and clinical data systems, such as electronic data capture (EDC) solutions, can increase patient safety and data accuracy, streamlining the overall clinical trial data collection and analysis process. For example, EHR data, such as demographics, medical history, and adverse events, can automatically populate electronic case report forms (eCRFs) in an EDC.

The advantages of interoperability in healthcare and life sciences are clear. As the use of EHR data in clinical trials becomes more prevalent, we need to ensure we’re proceeding in a cautious and compliant manner.

This FDA guidance document is a must-read for anyone involved in conducting clinical trials.

The Food and Drug Administration has issued a draft guidance stating that in order to see the highest success, clinical trial leaders must use electronic health records (EHR) that promote health data interoperability. In the draft guidance entitled Use of Electronic Health Record Data in Clinical Investigations, the FDA indicated that health data interoperability may be the key to enhancing clinical trials.

Obviously ensuring the clinical trial data is accurate and of the highest quality is something extremely important to the FDA as they use this data as part of its assessment for medications, treatments and drugs. The adoption of EHRs in healthcare and their potential reach are reasons the FDA believes they can help ensure the quality of clinical data.

The draft guidance, when finalized will represent the current thinking of the FDA and it provides recommendations on:

• Deciding whether and how to use EHRs as a source of data in clinical investigations
• Using EHRs that are interoperable with electronic systems supporting clinical investigations
• Ensuring the quality and the integrity of EHR data that are collected and used as electronic source datain clinical investigations

The FDA goes on to state that “EHRs may have the potential to provide clinical investigators and study personnel access to real-time and longitudinal health care data for review and can facilitate post-trial follow-up on patients to assess long-term safety and efficacy of medical products. There are also opportunities for long-term follow-up of large numbers of patients in studies where primary endpoints are rare, such as in prophylaxis studies,”

Clinical trial success heavily depends upon interoperable technology, and that includes interoperability between multiple EHRs to support better data transfer.

This is the second draft guidance issued by the FDA this year, in January they issued , Postmarket Management of Cybersecurity in Medical Devices, a draft guide on interoperability of medical devices.

The focus on interoperability in healthcare is a welcoming sight, but there is a lot of work that needs to be done in regards to defining interoperability and creating standards. It will be interesting to watch this all unfold.

In our next 2016 Connected Health trend we find ourselves with the call from patient advocates around the land: increased ownership of data by the patient.

The healthcare industry is the guardian of, in my opinion, the world’s most important data. With that being true, the patient data guardianship story is about to get even more interesting. This is because, in the coming year, we will see how shifts in power dynamics and healthcare consumer trends are making way for the increased ownership of data by patients.

Industry Shifts Cause Shifts in Power Dynamic

In the wake of Meaningful Use, we understand that, in theory, patients have a right to obtain their data. However, a few shifts in the marketplace, including some movements within the legal and regulatory world, are making it more and more clear that patients should have more access and ownership over their healthcare data.

At the core of this trend you find the Affordable Care Act, which, as it has taken root, caused a shift in power dynamics throughout the industry. Under old paradigms of care, the “power players” were the healthcare providers and health plans and, as such, the power players held much sway over the data. However, ACA is leading to some industry disruption, and healthcare consumers are emerging as power players in their own right.

Healthcare providers know that they need to engage healthcare consumers to motivate and incentivize them to manage their wellness instead of their sickness. Health plans know they need to motivate and incentivize cost savings behaviors. There are a lot of quality metrics and dollars at stake if the patient does not respond to the call, and this is what, in essence, is shifting the dynamic. In order to answer the call, the healthcare consumer is going to have increasing expectations for their relationship with their healthcare providers and health plans, and this brings with it expectations for increased ownership over their data.

How to Respond to this Shift

We are beginning to see this call for data in action. At the start of this year, HHS issued new guidelines due to a significant number of complaints received from healthcare consumers that were frustrated throughout the process of exercising their right to see their records. These guidelines stated that providers cannot require patients to state a reason for needing their records and they cannot deny access out of general concern that patients might be upset by the information.

However, there are more proactive ways to respond that will leave your patient population with the information they need to manage their wellness. At the foundation of answering the call of this trend we find the patient digital experience ripe with consumer-focused health management tools, products, and services. Fortunately, we can glean some interesting insights from a mobile health study designed by New York University and published in the Journal of Medical Internet Research last year. This study included more than 1,600 mobile phone users, assessing across sociodemographic characteristics, health app use history, reasons for use of health apps, and the status of their overall health. The study found that while mobile health app usage was extensive across the population, there are a vast number of people not engaging with those applications. What was the reason for the decrease in engagement? These apps were not meeting the healthcare consumers needs. What are they looking for? They were looking for their own data. The study found that these healthcare consumers wanted:

• Improved Communication with Providers: 57% of respondents were interested in apps capable of making an appointment or directly connecting to physicians. More than 60% wanted an app to access their medical records.
• Better Health Management Tools: Going beyond the “full access to my health records” study participants asked for tools that would help them “jot down symptoms that are ailing me….so I could send them to my doctor” and “graphs showing my health as time passes”.

I was chatting with my colleague Michael Porter about this study. Relating the desires of the healthcare consumer to Perficient’s experiences creating patient portals for Kaiser Permanente and Hospital Corporation of America, he saw the opportunity but also the “difficult but not insurmountable” challenges inherent to this trend. Here are some to keep in mind:

1. Make an appointment with my doctor: Doctors use a myriad of systems. Even clinics owned by the hospital use different scheduling systems than acute care hospital settings. This makes creating a digital means for appointment creation a challenge. While there are a number of 3rd party entrants trying to solve this problem, it remains a “beautiful but challenging Shangri-La”. While healthcare providers continue to work to make their data systems achieve such an end, many are succeeding in creating interim patient journeys that tie a digital “Make an Appointment” form with call center processes that are already in place.
2. Look at my health over time: The challenge here is that the underlying data is not currently captured in such a way as to create these views. In our current state we often expect our patients to be our manual ETL for their data across systems. While such a call is not impossible, the solution demands a rather robust data warehouse or big data lake and any given system will have to merge both records from the hospital and from the clinics.

With these data challenges in mind, it is actually pretty interesting to see the advantages that the unsuspecting health plans have in meeting these consumer data demands. As a central point of access, health plans are the owners of the most complete patient data story. As health plans become increasingly consumer centric, it is likely that health plans will be able the ones more apt to create such data centric solutions.

Increased Ownership of Data by Patients is just one of the trends we explore in our new guide, The Definitive Guide to Connected Health 2016: 10 Trends You Need to Know. Download the guide to see where this trend falls and to discover the other Connected Health trends healthcare executives must be aware of. In the guide we also provide insights to help organizations not only survive – but thrive – in the age of consumer-driven healthcare.

Real-world data, the data generated about drugs and devices in real-world settings (e.g., routing doctor visits, hospital stays, etc.), can help develop more accurate product profiles. This is because the data comes from products that are no longer confined to the smaller, homogenous populations involved in clinical trials. More comprehensive profiles can help better educate sponsors, hospitals, doctors, and patients on the effectiveness and safety of the medicine they’re taking, leading to better health outcomes. 

Download our guide to learn about 10 life sciences trends that we can expect to see this year

While pharmacovigilance systems are now standard in life sciences, companies are just beginning to leverage them together with other systems to tap into real-world data. By connecting a variety of post-trial data sources, such as medical records, insurance claims data, and safety data, life sciences companies are attempting to answer questions that might otherwise remain mysteries.

Pfizer’s short video explains real-world data in more detail.

To learn about other new trends that we can also expect to see in 2016, fill out the form below or click here.

Patients who are being treated for diseases, such as cancer, often require multiple drugs to manage their conditions. For example, patients are often prescribed drugs that treat the actual disease, while others are meant to manage side effects from those drugs. Many of these patients can probably attest to inquiring about or doing their own research on the possible harmful interactions between the drugs they’re taking.

According to the Center for Disease Control and Prevention (CDC), the number of patients who take multiple prescriptions has steadily increased since the 1980s. By 2012, 21% of all U.S. residents and 65% of all people over 65 were taking three or more drugs. 10% of all U.S. residents were taking five or more drugs. 

In an attempt to uncover problematic drug interactions, the Chicago Tribune, along with scientists and researchers at Columbia University Medical Center, conducted a study leveraging algorithms, the FDA’s Adverse Event Reporting System (FAERS), and electronic health records. While the results would need be further validated, researchers have found four drug combinations thus far that could lead to fatalities.

The use of algorithms and existing data sources to identify signals continues to gain traction in an industry that is hungry to keep patients safe. UCLA researchers recently shared a study that leveraged algorithms to scour electronic health records and flag patients who are at risk of becoming diabetic.

According to an article in Clinical Leader that featured Bayer Healthcare’s VP Global Strategy & Development Advisor, by evaluating certain criteria against patient populations, electronic health record (EHR) systems can help sponsors determine trial feasibility and shape better study protocols. This can reduce the number of amendments, which can cost anywhere from $300,000 to $500,000 each. 

EHR data might also dissuade sponsors from attempting certain trials, due the lack of specific patient populations. This in itself could prevent companies from having to initiate study start-up activities, including technology enablement, site training, and shipping products, thereby reducing the overall cost of drug development.

Expect to see a spike in EHR investments over the next few years, especially integrations between EHR and electronic data capture (EDC) systems.

To learn about other new trends that we can also expect to see in 2016, fill out the form below or click here.

Recruiting subjects for clinical trials has been a longstanding challenge for pharmaceutical and research organizations. According to Dr. Steven Alberts, Chair of Medical Oncology with the Mayo Clinic, only 5% of cancer patients ever enroll in a trial. On top of that, only a fraction of all trials ever finish enrolling enough patients on time and, in some cases, they never even get off the ground due to poor enrollment. More companies will begin leveraging new technology to match potential subjects to clinical trials. 

For example, IBM’s Watson can help analyze a patient’s medical and clinical attributes, compare them to various study protocols, and then determine patient/trial eligibility. Watson can even indicate the particular qualities that may have excluded the patient, giving clinicians the opportunity to help increase eligibility.

Other applications, such as TrialReach, will play a larger role in clinical trial recruitment efforts. TrialReach is a company focused on helping patients find the clinical trials they’re eligible for, based on a series of questions. Appealing directly to the public and empowering patients to take an interest in their own health research can be an effective method of improving clinical trial enrollment.

Recruiting subjects at point-of-care will likely become a focus area for academic medical centers. One scenario could look like this: A patient’s EHR record matches a specific trial’s inclusion criteria, so an alert pops up on the provider’s computer, at which point the provider discusses the trial with the patient. If the patient is interested, the provider clicks a button that sends a message to the study coordinator, who then connects with the patient, obtains consent, and completes enrollment. A similar recruitment approach can also be applied to patient portals.

To learn about other new trends that we can also expect to see in 2016, fill out the form below or click here.