What takes precedence? GCP or hospital network security?

patient compliance in medical clinical device trials

This is a piece I wrote a while back on my medical device security blog – Cybersecurity for medical devices.

One of the biggest challenge of using connected medical devices in clinical trials is near real-world usage of devices that are not commercially-ready.

We have a couple of customers that are performing clinical trials of medical devices in the ER and ICU. The tradeoffs between cybersecurity and patient safety are not insignificant.

What takes precedence? GCP or hospital network security?

Data quality, protocol compliance and patient safety are the 3 main pillars of GCP.

What is more important – patient safety or the health of the enterprise hospital Windows network?

What is more important – writing secure code or installing an anti-virus?

In order to answer these question, we performed a threat analysis on a medical device being studied in intensive care units.  The threat analysis used the PTA (Practical threat analysis) methodology.

Risk analysis of a medical device

Our analysis considered threats to three assets: medical device availability, the hospital enterprise network and patient confidentiality/HIPAA compliance. Following the threat analysis, a prioritized plan of security countermeasures was built and implemented including the issue of propagation of viruses and malware into the hospital network (See Section III below).

Installing anti-virus software on a medical device is less effective than implementing other security countermeasures that mitigate more severe threats – ePHI leakage, software defects and USB access.

A novel benefit of our approach is derived by providing the analytical results as a standard threat model database, which can be used by medical device vendors and customers to model changes in risk profile as technology and operating environment evolve. The threat modelling software can be downloaded here.

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The gap between the proletariat and Medidata (or should I say Dassault)

We need a better UX before [TLA] integration

The sheer number and variety of eClinical software companies and buzzwords confuses me.
There is EDC, CTMS, IWRS, IVRS, IWRS, IRT, eSource, eCOA, ePRO and a bunch of more TLAs.
For the life of me I do not understand the difference between eCOA and ePRO and why we need 2 buzzwords for patient reporting.

Here is marketing collateral from a CRO.   As you will see – they miss the boat on all the things that are important for site coordinators and study monitors.

We adapt responsively to change in your clinical trial to minimize risk and drive quality outcomes. Clinical research is complicated and it’s easy to get off track due to inexperienced project leaders, inflexible workflows, or the failure to identify risks before they become issues. We derive expert insights from evidence-based processes and strategic services to be the driving force behind quality outcomes, including optimized data, patient safety, reduced time-to-market, and operational savings.

What CRCs and CRAs have to say about the leading eClinical solutions

I recently did an informal poll on Facebook of what problems the CRA/CRC proletariat have to deal with on the job.

I want to thank Tsvetina Dencheva for helping me grok and distill people’s complaints
into 3 central themes.

Theme no. 1 – enter data once

Enable administrators to enter data once and have their authorized user lists, sites and metrics update automatically without all kinds of double and triple work and fancy import/export footwork between different systems. Failing a way of managing things in one place –
at least have better integration between the EDC and the CTMS.

The IT guys euphemistically call this problem information silos. I’ve always thought that they used the word silos (which are used to store animal food) as way of identifying with people who farm, without actually having to get their hands dirty by shovelling silage (which is really smelly btw).

I understand the rationale for having a CTMS and an EDC about as much as I understand the difference between eCOA and ePRO.

Here is some raw data from the informal Facebook survey

If I enter specific data, it would be great if there’s an integrated route to all fields connected to the said data. An easy example is – if I enter a visit, it transfers to my time sheet.

Same goes to contact reports. Apps! All sorts of apps, ctms, verified calculators, edc, ixrs, Electronic TMF. The list goes on and on. How could I forget electronic training logs? Electronic all sorts of log.

There are a lot of things we do day to day that are repetitive and can take away from actually moving studies forward. Thinking things like scanning reg docs, auto capturing of reg doc attributes (to a point), and integration to the TMF. Or better system integration, meaning where we enter a single data point (ie CTMS) and flowing to other systems (ie new site in CTMS, create new site in TMF. Enrolment metrics from EDC to CTMS) and so on.

If only the f**ing CTMS would work properly.

Theme number 2 – single sign-on.

The level of frustration with having to login to different systems is very high. The ultimate solution is to use social login – just login to the different systems with your Google Account and let Google/Firebase authenticate your identity.

Theme number 3 – data integrity

EDC edit check development eats up a lot of time and when poorly designed generates thousands of queries. Not good.

There is a vision of an EDC that understands the data semantics from context of the study protocol.

This is a very cool and advanced notion.

One of the study monitors put it like this:

The EDC should be smart enough to identify nonsense without having to develop a bunch of edit checks each time and have to deal with queries.

The EDC should be able to calculate if a visit is in a proper time window, or if imaging is in a proper time window. Also for oncology if RECIST 1.1 is used, then the EDC should be able to calculate: Body Surface Area, correct dosing based on weight and height of a patient, RECIST 1.1 tumor response and many other things that simply can be calculated.

About flaskdata.io

We specialise in faster submission for connected medical devices. We can shorten your
time to market by 9-12 months with automated patient compliance detection and response.

Call us and we’ll show you how. No buzzwords required.

Living in an ideal world where the study nurse isn’t overwhelmed by IT

Tigran examines the idea of using EDC edit checks to assure patient compliance to the protocol.

How should I assure patient compliance to the protocol in a medical device trial?

I get asked sometimes whether automated patient compliance deviation detection and response  is not overkill.

After all, all EDC systems allow comparing input to preset ranges and data types (edit checks). Why not use this, already available off the shelf functionality, to catch non-compliance? As Phileas Fogg put it: “Learn to use what you have got, and you won’t need what you have not”.

Why edit checks are not enough

There are 4 issues with using EDC edit checks to enforce patient compliance:

Individual variations

The original purpose of edit checks is to catch data entry mistakes. As they are generated automatically, they need to be robust enough not to fire indiscriminately. The effect non-compliance has on clinical data can be far less clearcut. This is especially true when taking individual variation between patients into account.

Timing

Even if we were able to reliably catch non-compliance through clinical data alone, there’s the issue of timing.

Each hour of delay between non-compliance event and a prompt to return to compliance devalues the prompt. Delays could come from a) manually entering source data into EDC, b) edit check firing in batch mode rather than during data entry, c) the time needed to process the edit checks.  What’s the benefit of being told you were not compliant one week ago?

Talk of closing the stable door after the horse has bolted…

By the time the nurse contacts the patient, the damage has already been done. No reinforcement is possible, as a patient could (theoretically) be reminded about the need to be compliant with the interval of several weeks – in which case this will serve as a token reminder, nothing more.

The study nurse may not have spare time on her hands

Let’s assume we live in an ideal world, where the study nurse isn’t overwhelmed by thousands of edit checks firing for no reason, and where data flows into EDC with no delay.

Even if this is true, there’s still the small matter of actually reaching out to the patient. When compliance reaches 90% that’s considered a good result – so in the best case scenario, the nurse would need to reach out to patients in 10% of cases. Edit checks are meant to be resolved immediately. If the EDC used fires edit checks during data entry, then the data entry process will be paralyzed. If edit checks are fired in the background, then the whole data cleaning/query resolution process would stall.

Edit checks are not an operational tool

What would happen in reality, though, is that any edit checks introduced to monitor patient compliance would be overridden by site staff. Together with any legitimate edit checks designed to keep the errors out. Resulting in the same level of compliance and much dirtier database. And that’s best case scenario, if otherwise no data would be entered at all.

Tigran Arzumanov is an experienced business development/sales consultant running BD as a service, a Contract Sales Organization for Healthcare IT and Clinical development.

Patient compliance – the billion dollar question

The high failure rate of drug trials

The high failure rate of drugs in clinical trials, especially in the later stages of development, is a significant contributor to the costs and time associated with bringing new molecular entities to market. These costs, estimated to be in excess of $1.5 billion when capitalized over the ten to fifteen years required to develop a new chemical entity, are one of the principal drivers responsible for the ongoing retrenchment of the pharmaceutical industry. Therapeutic areas such as psychiatry, now deemed very high risk, have been widely downsized, if not abandoned entirely, by the pharmaceutical industry. The extent to which patient noncompliance has marred clinical research has in some cases been underestimated, and one step to improving the design of clinical trials may lie in better attempts to analyze patient compliance during drug testing and clinical development. Phil Skolnick, Opiant Pharmaceuticals The Secrets of a successful clinical trial, compliance, compliance, compliance.

Compliance, compliance, compliance

Compliance is considered to be key to success of a medical treatment plan. (1, 2, 3)

It is the “billion dollar question” in the pharma and medical device industry.

In home-use medical devices in particular and in chronic diseases in general – there is wide consensus that patient compliance is critical to the success of the clinical trial.   Our experience with Israeli innovative medical device vendors is that they understand the criticality of patient compliance. They “get it”.

However, as Skolnick et al note – patient compliance with the clinical protocol is often underestimated in drug trials.

There are 4 challenges for assuring patient compliance in medical device trials.

1. The first challenge is maintaining transparency.    An executive at IQVIA noted (in a personal conversation with me) that IQVIA does not calculate patient compliance metrics since they assume that patient compliance is the responsibility of the sites.    The sponsor relies on the CRO who does not collect the metrics who relies on the sites who do not share their data.

2. The second challenge is having common standard metrics of compliance. Site performance on patient compliance may vary but if sites do not share common metrics on their patients’ compliance, the CRO and the sponsor cannot measure the most critical success factor of the study.

3. The third challenge is timely data.   In the traditional clinical trial process, low-level data queries are resolved in the EDC but higher-level deviations often wait until study-closeout.  The ability of a study team to properly resolve thousands of patient compliance issues months (or even years) after the patient participated is limited to say the least

4. The final and fourth challenge is what happens after the clinical trial.  How do we take lessons learned from a controlled clinical trial and bring those lessons into evidence-based practice?

A general approach to measuring and sharing patient compliance metrics

A general approach to addressing these challenges should be based on standard metrics, fast data and active monitoring and reinforcement and reuse. 

1. Use standard metrics for treatment and patient reporting compliance. The metrics then become a transparent indicator of performance and a tool for improvement.

A simple metric of compliance might be a score based on patient reporting, treatment compliance and treatment violations. We may consider a threshold for each individual metric – for example a 3 strike rule like in baseball.

A more sophisticated measure of compliance might be similar to beta in capital market theory where you measure the ‘volatility’ of individual patient compliance compared to the study as a whole. (Beta is used in the capital asset pricing model, which calculates the expected return of an asset based on its beta and expected market returns or expected study returns in our case).

2. Fast data means automating for digital data collection from patients, connected medical devices and sites eliminating paper source and SDV for the core data related to treatment and safety endpoints.

3. Actively monitor and help patients sustain a desired state of compliance to the treatment protocol, both pharmacologic and non-pharmacologic. Not everything is about pill-counting. This can be done AI-based reminders using techniques of contextual bandits and decision trees.

4. Reuse clinical trial data and extract high quality training information that can be used for evidence-based practice.

Patient compliance teardown

Measures of patient compliance can be classified into 3 broad categories:

Patient reporting – i.e how well patient reports her own outcomes

1. Treatment compliance – how well the treatment conforms to the protocol in terms of dosing quantities and times of application. 2. Research suggests that professional patients may break the pill counting model

3. Patient violations – if the patient does something contrary to the protocol like taking a rescue medication before the migraine treatment

Confounding variables

Many heart failure patients are thought to be non-compliant with their treatment because of prior beliefs – believing that the study treatment would not help them. In the European COMET trial with over 3000 patients it was found that a Lack of belief in medication at the start of the study was a strong predictor of withdrawal from the trial (64% versus 6.8%; p < 0.0001). Those patients with very poor well-being and limited functional ability (classified as NYHA III–IV) at baseline significantly (p = 0.01) increased their belief in the regular cardiac medication but not in their study medication (4)

But numerous additional factors also contribute to patient non-compliance in clinical trials:  lack of home support, cognitive decline, adverse events, depression, poor attention span, multiple concomitant medications, difficulty swallowing large pills, difficult-to-use UI in medical devices and digital therapeutics and inconveniences of urinary frequency with diuretics for heart failure patients (for example).

It seems that we can identify 6 main confounding variables that influence compliance:

1. Patient beliefs – medication is useless, or this specific medication cannot help or this particular chronic condition is un-curable

2. Concerns about side effects – this holds for investigators and for patients and may account for levels of PI non-compliance.

3. Alert fatigue – patients can be overwhelmed by too many reminder message

4. Forgetfulness – old people or young persons. Shift workers.

5. Language –  the treatment instructions are in English but the patient only speaks Arabic.

6. Home support – patient lives alone or travels frequently or does not have strong support from a partner or parent for their chronic condition.

Summary

Flaskdata.io provides a HIPAA and GDPR-compliant cloud platform that unifies EDC, ePRO, eSource and connected medical devices with automated patient compliance monitoring. The latest version of Flaskdata.io provides standard compliance metrics of patient reporting and active messaging reminders to help keep patients on track.  Your users can subscribe to real-time alerts and you can share metrics with the entire team.

Contact Batya for a free demo and consult and learn how fast data, metrics and active reinforcement can help you save time and money on your next study.

References

1. Geriatr Nurs. 2010 Jul-Aug;31(4):290-8. Medication compliance is a partnership, medication compliance is not.
Gould E1, Mitty E. https://www.ncbi.nlm.nih.gov/pubmed/20682408

2. Depression Is a Risk Factor for Noncompliance With Medical Treatment: Meta-analysis of the Effects of Anxiety and Depression on Patient compliance. DiMatteo et al http://jamanetwork.com/journals/jamainternalmedicine/fullarticle/485411

3. Importance of medication compliance in cardiovascular disease and the value of once-daily treatment regimens. Frishman. https://www.ncbi.nlm.nih.gov/pubmed/17700384

4. Adherence and perception of medication in patients with chronic heart failure during a five-year randomised trial Ekman, Andersson et al. https://doi.org/10.1016/j.pec.2005.04.005

 

 

 

Invisible gorillas and detection of adverse events in medical device trials

Weekly Episode #1 - Patients and study monitors are both people.

What is easier to detect in your study – Slow-moving or fast moving deviations?

This post considers human frailty and strengths.

We recently performed a retrospective study of the efficacy of  Flaskdata.io automated study monitoring in orthopedic trials. An important consideration was the ability to monitor patients who had received an implant and were on a long term follow-up program. Conceptually, monitoring small numbers of slow-moving, high-risk events is almost impossible to do manually since we miss a lot of what goes on around us, and we have no idea that we are missing so much. See the invisible gorilla experiment for an example.

One of patients in the study had received a spinal implant and was on a 6 month follow-up program dived into a pool to swim a few laps and died by drowning despite being a strong swimmer. Apparently, the pain caused by movement of the insert resulted  in loss of control and a severe adverse event. The patient had disregarded instructions regarding strenuous physical activity and the results were disastrous. 

It seems to me that better communications with the patients in the medical device study could have improved their level of awareness of safety and risk and perhaps avoided an unnecessary and tragic event.

Subjects and study monitors are both  people.

This might be a trivial observation but I am going to say it anyhow, because there are lessons to be learned by framing patients and monitors as people instead of investigation subjects and process managers. 

People are the specialists in their personal experience, the clinical operations team are the specialists in the clinical trial protocol. Let’s not forget that subjects and study monitors are both  people.

Relating to patients in a blinded study as subjects without feelings or experience is problematic. We can relate to patients in a personal way without breaking the double blinding and improve their therapeutic experience and their safety. 

We should relate to study monitors in a personal way as well, by providing them with great tools for remote monitoring and enable them to prioritize their time on important areas such as dosing violations and sites that need more training. We can use analytics of online data from the EDC, ePRO and eSource and connected medical devices in order to enhance and better utilize clinical operations teams’ expertise in process and procedure.

A ‘patient-centered’ approach to medical device clinical trials

In conditions such as Parkinsons Disease, support group meetings and online sharing are used to stay on top of medication, side effects, falls and general feeling of the patient even though the decisions on the treatment plan need to be made by an expert neurologist / principal investigator and oversight of protocol violations and adverse events is performed by the clinical operations team. There are many medical conditions where patients can benefit by taking a more involved role in the study. One common example is carpal tunnel syndrome. 

According to the findings of an August 3rd, 2011 issue of the Journal of Bone and Joint Surgery (JBJS), patients receiving treatment for carpal tunnel syndrome (CTS) prefer to play a more collaborative role when it comes to making decisions about their medical or surgical care. 

Treatment of carpal-tunnel syndrome which is very common and also extremely dependent upon patient behavior and compliance is a great example of the effectiveness of “shared decision-making, or collaborative, model” in medicine, in which the physician and patient make the decision together and exchange medical and other information related to the patient’s health.

As the article in JBJS concludes:

“This study shows the majority of patients wanted to share decision-making with their physicians, and patients should feel comfortable asking questions and expressing their preferences regarding care. Patient-centered care emphasizes the incorporation of individual styles of decision making to provide a more patient-centered consultation,” Dr. Gong added. 

In a ‘patient-centered’ approach to medical device clinical trials, patients’ cultural traditions, personal preferences and values, family situations, social circumstances and lifestyles are considered in the decision-making process.

Automated patient compliance monitoring with tools such as Flaskdata.io are a great way to create a feedback loop of medical device clinical data collection,  risk signatures improvement, detection of critical signals and communications of information to patients. Conversely, automated real-time patient compliance monitoring is a a great way of enhancing clinical operations team expertise.

Patients and study monitors are both people. 

Important EDC features for medical device clinical trials

esource tp get smart to market

Medidata Rave and its CTMS companion product iMedidata are a far more comprehensive solution than OpenClinica but when you choose EDC software for medical device clinical trials, you enter a realm of unique requirements involving connectivity, security, privacy, API integration and specific interfaces to hardware.

Electronic data capture software (EDC software) systems have demonstrated that their value is manifest for clinical trial efficiency and cost savings. Soon, medical device clinical trials will no longer be dependent upon paper-based systems at all. Paper is great for passing notes in class, but how often does that even happen anymore? Kids in school are more likely to use Snapchat or simply text each other.

As someone said recently – “millenials are off Facebook, adults still use email”.

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Homeostasis and medical device clinical trials

medical device clinical trials

Danny talks about how to strike a good balance between people and technology for monitoring medical device clinical trials.

Are real-time alerts too much of a good thing for monitoring your study? Maybe real-time alerts for patient compliance in medtech studies is just a fad – a fad just like WhatsApp.

I had a conversation with my friend John who has worked for years in digital technologies in the public education space. With over a billion people on social media, John was concerned that the human element is getting trashed.

My answer to him was – “No way”. People, both individually and collectively after they go through a change (especially a big technology change) they tend to return to a state of homeostasis.

The homeostasis of information

Stop for a moment and consider how much of your data sharing and private messaging interaction is digital and how much is paper and then ask yourself why clinical trial compliance monitoring is still dependent upon paper interactions.

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Why medical device studies need business controls

patient compliance in medical clinical device trials

There are some interesting analogies between cyber security and medical device clinical trials from a risk management perspective. Both areas are complex, vulnerable to human exploits and may result in loss of data.

Medical device trials are not exempt from unexpected human behavior.

Despite this concern, I find it significant that guidance for remote-risk-based monitoring of global multi-center clinical trials does not consider business controls for human resources, internal audit, and information security.

In this post, we consider the importance of study monitoring from a non-regulatory perspective of business risk.

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10 ways to reduce clinical trial risk and they are all free

Are the lights on but no one home in your medical device clinical trial?

Collecting low-quality data means that your trial is likely to fail. You will not be able to prove or disprove the scientific hypothesis of your medical device clinical trial. You will have wasted your time.

You cannot outsource quality, you have to build it into the trial design

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How to overcome 5 eSource implementation challenges

Jenya wrote a piece about the challenges of clinical trials operations change management for regulatory people who have to work with medical technology developers and I just had to write my own intro.

Frankly, its easier to talk about change for other people than for yourself. A lot easier.  I have written here, here and here about the gaps between the stakeholders in medical device clinical trials – security, IT, engineering, product marketing ,regulatory affairs and medical device security to name a few.

Overlook change management at your own risk

Change management is a topic usually overlooked when medtech companies implement cloud EDC, and introduce medical IoT for collecting data from patients directly and use electronic source documents for their connected device/mobile medical app or device clinical trial.

In this post, Jenya talks about how to manage change during the transition from traditional medical device clinical trial data management to cloud technologies, remote monitoring, medical IoT and electronic source data.

So enjoy.

Danny

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