How to swim in the cold water of hybrid trials



(The water in the pool in Cascais in October was < 12 degrees)

There are over 135 COVID-19 vaccine candidates in the pipeline at the time of writing this post

Although there are always personal, political and emotional preferences, we should probably not select a clinical data management platform like we choose villas in exotic vacation spots.

The reality is that simplification of the protocol and the study conduct will have a much bigger impact on time to completion than choice of a particular eClinical platform.

COVID-19 has much wider ramifications for the clinical trial industry beyond the next 12-18 months.

COVID-19 signals a driving concern to use technology to acquire valid data from clinical trials in the fastest way possible.

Virtual clinical trials

One approach is to recruit and collect data from patients online in what is called a virtual trials model.

There is a gigantic amount of buzz on virtual clinical trials because of COVID-19. The idea is to go direct to patient with digital tools and engagement. This is theoretically supposed to cut out all the friction for recruitment and and overhead of research sites.

With all the buzz on virtual trials, no one seems to know how many virtual trials are actually being conducted. (There is a well-known axiom that technology adoption is inversely proportional to PR).

It may be that in the future, fewer than 5% of trials would remain all paper. Maybe 5%, will go fully virtual.

Hybrid clinical trials

The action will be in the middle in “hybrid”. Trials are moving away from paper, “virtualizing” a process here, a step there. I am looking to see how much of this is taking place, and to what extent COVID-19 accelerates it, and which processes and steps are virtualizing the fastest.

Based on observing 12 hybrid trials running right now on the flaskdata.io platform, today, I can assert that hybrid trials are complex distributed systems with a whole new set of challenges that make the old site/investigator-centric model look like a stroll in the park.

Connected medical device vendors understand the value of merging patient, clinical and device data into real-time data streams.  Once you have a real-time data stream, you can use real-time automated monitoring.

However, bringing merged real-time streams of patient, device and clinical investigator data into the domain of mainstream drug trials is hugely challenging because the data sources are highly heterogenous.

Combining patient outcome reporting with mobile apps, passive data collection from wearables and phones and site monitor data entry creates a complex distributed system of data sources.   Such a complex distributed system cannot possibly be monitored by assuming that there is a single paper source document. That assumption is no longer valid.

Observability of events

We need to correlate and group events across different systems, applications and users. We need to achieve low level observability of a patient while attributing it to top level cohorts and sites in the study.

This is especially difficult since the different EDC systems and digital appliances were not designed for monitoring.

You can see a presentation here on using pivot tracing for dynamic causal monitoring of distributed systems. This is work done by Jonathan Mace while he was a PhD student at Brown.   The work was done on HDFS but the concepts are applicable to virtual and hybrid clinical trials.

flaskdata.io  is a cloud platform that automates detection of deviations in clinical trials using these general concepts.

Flask provides an immediate picture of what’s going on. The picture can then be grouped by patient, physician, principal investigator, project managers all the way up to the  VP Clinical and CEO. In political terms, you might say that  we  democratize the process of observing clinical trials using metrics and automation.

Automation can be used to speed delivery of valid data to decision makers in clinical trials. The basic idea is to monitor with alerts.  Some of the ideas from the talk:

Alerts are metrics over/under a threshold

Alerts are urgent, important, actionable and real
In the world of alerts, symptoms are better than causes
Validate: Are we calculating the right metric?
Verify: Are we calculating the metric right?

Do it Fast. fast. fast.

You can see my talk here: Automated Detection and response for clinical trials.

Originally published on medium.com

Home alone and in a clinical trial

1 in 7 American adults live alone

What is atherothrombosis?

If you are age 40 to 60 and live alone, your CV system is at high risk

Can social networking mitigate the risk of living alone?

Social networks detach people from meaningful interactions with one another

We expect more from technology and less from each other

Digital technology enables real interactions with real primary care teams.

This was first published on Medium at Can digital mitigate the risk of living alone

Hack back the user interface for clinical trials

As part of my campaign for site-coordinator and study-monitor centric clinical trials; we first need to understand how to exploit a vulnerability in human psychology.

As a security analyst, this is the way I look at things – exploits of vulnerabilities.

In 2007, B.J. Fogg, founder and director of the Stanford Behavior Design Lab taught a class on “mass interpersonal persuasion. A number of students in the class went on to apply these methods at Facebook, Uber and Instagram.

The Fogg behavior model says that 3 things need to happen simultaneously to initiate a behavior: Motivation (M), ability (A) and a trigger (T).

When we apply this model to patient-centric trials, we immediately understand why patient-centricity is so important.

Motivation – the patient wants therapy (and may also be compensated for her participation).

Ability is facility of action. Make it easy for a patient to participate and they will not need a high energy level to perform the requisite study tasks (take a pill, operate a medical device, provide feedback on a mobile app).

Without an external trigger, the desired behavior (participating in the study in a compliant way) will not happen.  Typically, text messages are used to remind the patient to do something (take treatment or log an ePRO diary).  A reminder to log a patient diary is a distraction; when motivation and ability exceed the trigger energy level, then the patient will comply. If the trigger energy level is too high (for example – poor UX in the ePRO app) then the patient will not comply.    Levels of protocol adherence will be low.

The secret is designing the study protocol and the study UX so that the reminder trigger serves the patient and not the patient serving the system.

People-centric clinical trials

Recall – that any behavior ( logging data, following up) requires 3 things: motivation, ability and a trigger.

A site coordinator can be highly motivated. She may be well trained and able to use the EDC system even the UX is vintage 90s.

But if the system doesn’t give anything back to her; reminders to close queries or to follow-up are just distractions.

The secret is designing the study protocol and the study UX so that the reminder trigger serves the CRC and CRA and not the CRC, CRA are serving the system.

When we state the requirement as a trigger serving the person – we then understand that it is not about patient-centricity.

It is about people-centricity.

 

 

A better tomorrow for clinical trials

A better tomorrow – Times of crisis usher in new mindsets

By David Laxer. Spoken from the heart.

In these trying days, as we adjust to new routines and discover new things about ourselves daily, we are also reminded that the human spirit is stronger than any pandemic and we have survived worse.

And because we know we’re going to beat this thing, whether in 2 weeks or 2 months, we also know that we will eventually return to normal, or rather, a new normal.

In the meantime, the world is showing a resolve and a resilience that gives us much room to hope for a better tomorrow for developing new therapeutics.

However, these days have got us wondering how things might have looked if clinical trials were conducted differently. It’s a well-known fact that clinical trials play an integral role in the development of new, life-saving drugs, but by the time they get approved by the FDA it takes an average of 7.5 years and anywhere between $150m-2bn per drug.

Reasons for failure

Many clinical studies still use outdated methods for data collection and verification: they still use a fax for crying out. They continue to manually count leftover pills in bottles, and still rely on patients’ diary entries to ensure adherence.

Today, the industry faces new challenges to recruit enough participants as COVID-19 forces people to stay at home and out of research hospital sites. 

Patient drop-outs, adverse events and delayed recording of adverse events  are still issues for pharma and medical device companies conducting clinical research.  The old challenge of creating interpretable data to examine safety and efficacy of new therapeutics remain.

The Digital Revolution:

As hard as it is to believe, the clinical trial industry just might be the last major industry to undergo digital transformation..

As every other aspect of modern life has already been digitized, from banking to accounting to education now, more than ever, is the time to accelerate the transition of this crucial process, especially as we are painfully reminded of the need for finding a vaccine.  Time is not a resource we can waste any longer.

Re-imagining the future

When we created FlaskData we were primarily driven by our desire to disrupt the clinical trial monitoring paradigm  and bring it into the 21st century — meaning real-time data collection and automated detection and response. From the beginning we found fault in the fact that clinical trials were, and still are overly reliant on manual processes  and this causes unacceptable delays in bringing new and essential drugs and devices to market. These delays, as we are reminded during these days, not only cost money and time, but ultimately they cost us lives.

To fully achieve this digitization it’s important to create a secure cloud service that can accelerate the entire  process, and provide sponsors with an immediate picture and interpretable data without having to spend 6-12 months cleaning data.  This is achieved with real-time data collection, automated detection and response and an open API that enables any healthcare application to collect clinical-trial-grade data and assure patient adherence to the clinical protocol.

Our Promise:

It didn’t take a virus to make us want to deliver new medical breakthroughs into the hands that need them most, but it has definitely made us double down on our resolve to see it through. The patient needs to be placed at the center of the clinical research process and we are tasked to reduce the practical, geographical and financial barriers to participation. The end result is a more engaged patient, higher recruitment and retention rates, better data and reduced study timelines and costs.

The Need For Speed

As the world is scrambling to find a vaccine for Corona, we fully grasp 2 key things: 1) Focus on patients and 2) Provide clinical operations teams with the ability to eliminate inefficiencies and move at lightning speed. In these difficult times, there is room for optimism as it is crystal clear, just how important it is to speed up the process.

 

Social Distancing

In this period of social distancing, we can only wonder about the benefits of conducting clinical trials remotely. We can only imagine how many trials have been rendered useless as patients, reluctant to leave their houses have skipped the required monitoring, have forgotten to take their pills and their diary entries have gotten lost amidst the chaos.

With a fully digitized process for electronic data collection, social distancing would have no effect on the clinical trial results.

About David Laxer

David is a strategist and story-teller. He says it best – “Ultimately, when you break it down, I am a storyteller and a problem solver. The kind that companies and organizations rely on for their brand DNA, culture and long-lasting reputation”.

 

Reach out to David on LinkedIn

I love being a CRA, but the role as it exists today is obsolete.

I think that COVID-19 will be the death knell for on-site monitoring visits and SDV.    Predictions for 2020 and the next generation of clinical research – mobile EDC for sites, patients and device integration that just works.

I’m neither a clinical quality nor a management consultant. I cannot tell a CRO not to bill out hours for SDV and CRA travel and impact study budget by 25-30% and delay results by 12-18 months.

Nope.   I’m not gonna tell CROs what to do.    Darwin will do that for me.

I develop and support technology to help life science companies go faster to market.  I want to save lives by shortening time to complete clinical trials for COVID-19 vaccine and treatments by 3-6 months.

I want to provide open access to research results – for tomorrow’s pandemic.

I want to  enable real-time data sharing.

I want to enable participants in the battle with COVID-19 to share real-world / placebo arm data, making the fight with COVID-19 more efficient and collaborative and lay the infrastructure for the next wave of pandemics.

I want to provide real-time data collection for hospitals, patients and devices.  Use AI-driven detection of protocol violations and automated response to enable researchers to dramatically improve data reliability, allowing better decision making and improving patient safety.

The FDA (a US government regulatory bureaucracy) told the clinical trial industry to use e-Source 10 years ago and to use modern IT .  If FDA couldn’t then maybe survival of the fittest and COVID-19 well do the job.

FDA’s Guidance for Industry: Electronic Source Data in Clinical Investigations, says, in part:
“Many data elements (e.g., blood pressure, weight, temperature, pill count, resolution of a symptom or sign) in a clinical investigation can be obtained at a study visit and can be entered directly into the eCRF by an authorized data originator. This direct entry of data can eliminate errors by not using a paper transcription step before entry into the eCRF. For these data elements, the eCRF is the source. If a paper transcription step is used, then the paper documentation should be retained and made available for FDA inspection.”

I loved this post by Takoda Roland on the elephant in the room.

Source data validation can easily account for more than 80% of a monitor’s time. You go on site (or get a file via Dropbox). Then you need  to page through hundreds of pages of source documents to ensure nothing is missing or incomplete. Make sure you check the bare minimum amount of data before you need to rush off to catch my flight, only to do it all again tomorrow in another city, I am struck with this thought: I love being a CRA, but the role as it exists today is obsolete.

Opinion: A Futurist View on the Use of Technology in Clinical Trials

 

Using automated detection and response technology mitigate the next Corona pandemic

What happens the day after?   What happens next winter?

Sure – we must find effective treatment and vaccines.  Sure – we need  to reduce or eliminate the need for on-site monitoring visits to hospitals in clinical trials.  And sure – we need to enable patient monitoring at home.

But let’s not be distracted from 3 more significant challenges:

1 – Improve patient care

2 – Enable real-time data sharing. Enable participants in the battle with COVID-19 to share real-world / placebo arm data, making the fight with COVID-19 more efficient and collaborative.

3- Enable researchers to dramatically improve data reliability, allowing better decision making and improving patient safety.

Clinical research should ultimately improve patient care.

The digital health space is highly fragmented (I challenge you to precisely define the difference between patient engagement apps and patient adherence apps and patient management apps).  There are over 300 digital therapeutic startups. We are lacking a  common ‘operating system and  there is a dearth of vendor-neutral standards that would enable interoperability between different digital health systems mobile apps and services.

By comparison – clinical trials have a well-defined methodology, standards (GCP) and generally accepted data structures in case report forms.  So why do many clinical trials fail to translate into patient benefit?

A 2017 article by Carl Heneghan, Ben Goldacre & Kamal R. Mahtani “Why clinical trial outcomes fail to translate into benefits for patients”  (you can read the Open Access article here) states the obvious: that the objective of clinical trials is to improve patients’ health.

The article points at  a number of serious  issues ranging from badly chosen outcomes, composite outcomes, subjective outcomes and lack of relevance to patients and decision makers to issues with data collection and study monitoring.

Clinical research should ultimately improve patient care. For this to be possible, trials must evaluate outcomes that genuinely reflect real-world settings and concerns. However, many trials continue to measure and report outcomes that fall short of this clear requirement…

Trial outcomes can be developed with patients in mind, however, and can be reported completely, transparently and competently. Clinicians, patients, researchers and those who pay for health services are entitled to demand reliable evidence demonstrating whether interventions improve patient-relevant clinical outcomes.

There can be fundamental issues with study design and how outcomes are reported.

This is an area where modeling and ethical conduct intersect;  both are 2 critical areas.

Technology can support modeling using model verification techniques (used in software engineering, chip design, aircraft and automotive design).

However, ethical conduct is still a human attribute that can neither be automated nor replaced with an AI.

Let’s leave modeling to the AI researchers and ethics to the bioethics professionals

For now at least.

In this article, I will take a closer look at 3 activities that have a crucial impact on data quality and patient safety. These 3 activities are orthogonal to the study model and ethical conduct of the researchers:

1 – The time it takes to detect and log protocol deviations.

2 – Signal detection of adverse events (related to 1)

3 – Patients lost to follow-up (also related to 1)

Time to detect and log deviations

The standard for study monitors is to visit investigational sites once ever 5-12 weeks.   A Phase IIB study with 150 patients that lasts 12 months would typically have 6-8 site visits (which incidentally, cost the sponsor $6-8M including the rewrites, reviews and data management loops to close queries).

Adverse events

As reported by Heneghan et al:

A further review of 11 studies comparing adverse events in published and unpublished documents reported that 43% to 100% (median 64%) of adverse events (including outcomes such as death or suicide) were missed when journal publications were solely relied on [45]. Researchers in multiple studies have found that journal publications under-report side effects and therefore exaggerate treatment benefits when compared with more complete information presented in clinical study reports [46]

Loss of statistical significance due to patients lost to follow-up

As reported by Akl et al in  “Potential impact on estimated treatment effects of information lost to follow-up in randomized controlled trials (LOST-IT): systematic review” (you can see the article here):

When we varied assumptions about loss to follow-up, results of 19% of trials were no longer significant if we assumed no participants lost to follow-up had the event of interest, 17% if we assumed that all participants lost to follow-up had the event, and 58% if we assumed a worst case scenario (all participants lost to follow-up in the treatment group and none of those in the control group had the event).

Real-time data

Real-time data (not data collected from paper forms 5 days after the patient left the clinic) is key to providing an immediate picture and assuring interpretable data for decision-making.

Any combination of data sources should work – patients, sites, devices, electronic medical record systems, laboratory information systems or some of your own code. Like this:

Mobile eSource mobile ePRO medical device API

Signal detection

The second missing piece is signal detection for safety, data quality and risk assessment of patient, site and study,

Signal detection should be based upon the clinical protocol and be able to classify the patient into 1 of 3 states: complies, exception (took too much or too little or too late for example) and miss (missed treatment or missing data for example).

You can visualize signal classification as putting the patient state into 1 of 3 boxes like this:Automated response

One of the biggest challenges for sponsors running clinical trials is delayed detection and response.   Protocol deviations are logged 5-12 weeks (and in a best case 2-3 days) after the fact.   Response then trickles back to the site and to the sponsor – resulting in patients lost to follow-up and adverse events that were recorded long after the fact..

If we can automate signal detection then we can also automate response and then begin to understand the causes of the deviations.    Understanding context and cause is much easier when done in real-time.        A good way to illustrate is to think about what you were doing today 2 weeks ago and try and connect that with a dry cough, light fever and aching back.   The symptoms may be indicative of COVID-19 but y0u probably don’t remember what you were doing and  with whom you came into close contact.     The solution to COVID-19 back-tracking is use of digital surveillance and automation. Similarly, the solution for responding to exceptions and misses is to digitize and automate the process.

Like this:

Causal flows of patient adherence

Summary

In summary we see 3 key issues with creating meaningful outcomes for patients:

1 – The time it takes to detect and log protocol deviations.

2 – Signal detection of adverse events and risk (related to 1)

3 – Patients lost to follow-up (also related to 1)

These 3 issues for creating meaningful outcomes for patients can be resolved with 3 tightly integrated technologies:

1 – Real-time data acquisition for patients, devices and sites (study nurses, site coordinators, physicians)

2 – Automated detection

3 – Automated response

 

 

 

 

10 ways to detect people who are a threat to your clinical trial

Flaskdata.io helps Life Science CxO teams outcompete using continuous data feeds from patients, devices and investigators mixed with a slice of patient compliance automation.

One of the great things about working with Israeli medical device vendors is the level of innovation, drive and abundance of smart people.

It’s why we get up in the morning.

There are hundreds of connected medical devices and digital therapeutics (last time I checked over 300 digital therapeutics alone).

When you have an innovative device with network connectivity, security and patient privacy, availability of your product and integrity of the data you collect has got to be a priority.

Surprisingly, we get a  range of responses from people when we talk about the importance of cyber security and privacy for clinical research,

Most get it but some don’t.   The people that don’t get it, seem to assume that security and privacy of patient data is someone else’s problem in clinical trials.

The people who don’t work in security, assume that the field is very technical, yet really – it’s all about people.   Data security breaches happen because people or greedy or careless.    100% of all software vulnerabilities are bugs, and most of those are design bugs which could have been avoided or mitigated by 2 or 3 people talking about the issues during the development process.

I’ve been talking to several of my colleagues for years about writing a book on “Security anti-design patterns” – and the time has come to start. So here we go:

Security anti-design pattern #1 – The lazy employee

Lazy employees are often misdiagnosed by security and compliance consultants as being stupid.

Before you flip the bozo bit on a site coordinator as being non-technical, consider that education and technical aptitude are not reliable indicators of dangerous employees who are a threat to the clinical trial assets.

Lazy employees may be quite smart but they’d rather rely on organizational constructs instead of actually thinking and executing and occasionally getting caught making a mistake.

I realized this while engaging with a client who has a very smart VP – he’s so smart he has succeeded in maintaining a perfect record of never actually executing anything of significant worth at his company.

As a matter of fact – the issue is not smarts but believing that organizational constructs are security countermeasures in disguise.

So – how do you detect the people (even the smart ones) who are threats to PHI, intellectual property and system availability of your EDC?

1 – Their hair is better organized then their thinking

2 – They walk around the office with a coffee cup in their hand and when they don’t, their office door is closed.

3 – They never talk to peers who challenge their thinking.   Instead they send emails with a NATO distribution list to everyone on the clinical trial operations team.

4 – They are strong on turf ownership.  A good sign of turf ownership issues is when subordinates in the company have gotten into the habit of not challenging the VP coffee-cup holding persons thinking.

5 – They are big thinkers.    They use a lot of buzz words.

6 – When an engineer challenges their GCP/regulatory/procedural/organizational constructs – the automatic answer is an angry retort “That’s not your problem”.

7 – They use a lot of buzz-words like “I need a generic data structure for my device log”.

8 – When you remind them that they already have a generic data structure for their device log and they have a wealth of tools for data mining their logs – amazing free tools like Elasticsearch and R….they go back and whine a bit more about generic data structures for device logs.

9 – They seriously think that ISO 13485 is a security countermeasure.

10 – They’d rather schedule a corrective action session 3 weeks after the serious security event instead of fixing it the issue the next day and documenting the root causes and changes.

If this post pisses you off (or if you like it),  contact  me –  always interested in challenging projects with challenged people who challenge my thinking.

Temperature excursions and APIs to reduce study monitor work

I did a lot of local excursions the past 3 days – Jerusalem, Tel Aviv, Herzliya and Haifa.   For some reason, the conversations with 2 prospects had to do with refrigerators.   I do not know if this is Freudian or not, considering the hot weather of July in Israel.

The conversations about refrigerators had to do with storing drugs / investigational product at the proper temperatures.

Temperature excursion is a deviation

The great thing about not coming from the clinical trials space is that you are always learning new things.

Yesterday – I learned that a Temperature excursion is a deviation from given instructions. It is defined in the WHO Model Guidance as “an excursion event in which a Time Temperature Sensitive Pharmaceutical Product (TTSPP) is exposed to temperatures outside the range(s) prescribed for storage and/or transport.

Storing drugs at the proper temperature is part of GCP. Here is an SOP for Monitoring and Recording Refrigerator & Freezer Temperatures

1 Introduction All refrigerators and freezers used for the storage of Investigational Medicinal Products (IMPs) must be temperature controlled, and continuously monitored and maintained within the appropriate ranges as defined by the protocol. ICH GCP Principle 2.13 states “Systems with procedures that assure the quality of every aspect of the trial should be implemented.”

Moving on:

5 Procedure
 Current maximum/minimum thermometers must be monitored as a minimum at least once on a daily basis on all working days, and recorded legibly on the temperature monitoring log.
 The digital maximum/minimum thermometer –
□ Should be read from the outside of the refrigerator without opening the door.
□ Have an accuracy of at least +/- 1 oC.
□ Be able to record temperatures to one decimal place.
□ Be supplied with a calibration certificate.
□ Have the calibration check on an annual basis.
 Temperature logs should be kept close to the refrigerator/freezer (but not inside) to which they relate for ease of reference, and should be clearly identified as relating to that appliance.
 A separate temperature record must be kept for each fridge/freezer. (The use of whiteboards as a method of logging results is not acceptable.)
 It is good practice to record the temperature at a similar time each day e.g., first thing in the morning before the refrigerator door is opened for the first time. This will allow review of trends in results recorded; help highlight any changes in temperatures recorded and deviation in refrigerator performance.

There is a lot of manual work involved looking at refrigerators

I believe a study monitor will spend 20’/day checking logs of refrigerator temperature readings. When you add in time for data entry to the site coordinators – that’s another 20’/day and then you have to multiply by the number of sites and refrigerators.   This is only the reading temperatures and capturing data to the EDC part of the job.   Then you have to deal with queries and resolving deviations.

For something so mundane (although crucial from a medical research perspective), its a lot of work. The big problem with using study monitors to follow temperature excursions is that the site visits are every 1-3 months. With the spiralling costs of people, the site visits are getting less frequent.

This means that it is entirely plausible that patients are treated with improperly stored drugs and the deviation is undetected for 3 months.

Whenever I see a lot of manual work and late event detection, I see an opportunity.

It seems that there are a few vendors doing remote monitoring of refrigerators.  A Polish company from Krakow, called Efento has a complete solution for remote monitoring of refrigerators storing investigational product.  It looks like this:

 

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What is cool (to coin a pun) about Efento is that they provide a complete solution from hardware to cloud.

The only thing missing is calling a Flask API to insert data into the eCRF for the temperature excursions.

Once’s we’ve got that, we have saved all of the study coordinators and study monitors time.

More importantly, we’ve automated an important piece of the compliance monitoring puzzle – ensuring that temperature excursions are detected and remediated immediately before its too late.

Doctor-Patient Communication – the key to success and the struggle to succeed.

Katherine Murphy, Chief Executive of the Patients Association London once said,

“The huge rise in complaints in relation to communication and a lack of respect are of particular concern. Patients are not receiving the compassion, dignity and respect which they deserve.”

As clinical trial technology guys, you would assume that we love code more than we love the patients and site coordinators who use our software.

I took a random sample of  home pages from 3 of our competitors – and this is what I found.   We can discuss if real-time visibility to  data is going to make the clinical operations team more effective or not – but that is a story for another post.

EMPOWER YOUR CLINICAL TRIAL EDC + ePRO and a bunch of other features to make your clinical trial successful. ( viedoc )

Oracle Health Sciences InForm. Accelerate Clinical Trial Timelines While Reducing Trial Cost and Risk.

Collect and deliver higher-quality data faster through advanced data capture and query management, real-time visibility to data, standards-based, integrated workflows, and security best practices.

Faster, smarter medical research. Castor is the end-to-end data solution, enabling researchers to easily capture and integrate data from any source on one platform. Thousands of medical device, biotech, and academic researchers around the world are using Castor EDC (Electronic Data Capture), ePRO, and eTMF to accelerate their studies.

In this article we’ll discuss the doctor-patient communications gap as a generic problem. We will briefly examine the root cause of the problem and conclude by proposing a light-weight easy-to-use Web service for sharing and private messaging with patients and physicians as a way to ameliorate the problem.

Poor patient-doctor communications as a generic problem

Doctor-Patient communication is the key to the success of a treatment plan and reduction of hospital readmission. However, doctors and nurses often fail in communicating with their patients properly.

What is the nature of poor doctor-patient communications?

Some patients say that their doctors need to polish their communication skills; although they are excellent diagnosticians.

Other patients say that their doctors know how to talk, but seem to have no time to listen.

Many patients also complain that their doctors don’t explain things in terms patients can understand. Poor communications between doctors/nurses and their patients can come at a high cost, creating misunderstandings that can  lead to malpractice suits.

In a hospital setting, we often hear that patients feel that they are not getting any useful information while the medical staff feel that they have taken the time to communicate all the data that the patients and their families need in order to understand and comply with the treatment plan.

The question is why some doctors find it hard to communicate properly and share things with their patients in a desired manner while other doctors succeed in communicating the therapeutic plan to the patient in a manner that the patient understands.

Poor physician-patient communications is rooted in cognitive and cultural gaps

Patients are the experts at their personal feelings and experiences.  Physicians are the experts in the medical science.  Cultural and language differences and preconceived notions about the doctors role only contribute to the cognitive gap between emotion and science.

Besides the cultural and cognitive gaps, high patient volume and work overload is another root contributor to poor doctor patient communications.  This generally happens in poor countries. In the third world, working over capacity is one of the biggest barriers to effective communication. Hospitals, doctors and nurses are forced to admit more and more patients and are compelled to handle more than they can manage. Under such circumstances, health professionals cannot devote enough time to their patients let alone sit down with them in a quiet corner and explain the therapeutic plan.

Sharing and private messaging with patients  and doctors helps bridge the gaps

The solutions are out there.

In this always-on age of mobile medical devices and cloud services, both healthcare professionals and the patients have immediate access to the latest solutions that can help them communicate more effectively and efficiently. There are private social networks for healthcare that have been exclusively developed for sharing and private messaging with doctors, nurses and patients, enabling doctors and patients to interact and share where and whenever they need the interaction.

Neither the patient nor the physician need to be tied down to a proprietary healthcare provider portal.

Secure Web-based sharing and private messaging services improve the ways doctors and nurses communicate with their patients. This helps them improve the quality of service and lower operational costs, and enables doctors to treat more patients in less time and with less stress.

In summary

Poor patient-doctor communications has a number of causes and it is rooted in both cultural, language and cognitive differences.   Using a neutral medium such as online sharing and private messaging with patients and doctors helps bridge the gaps we discussed.

We’d love to hear what you think – please comment!

Thanks!

Urban medical legends

Because I was trained as a solid-state physicist I am skeptical of many medical claims – including the efficacy of digital health apps.  Gina Kolata wrote this post last week.  I’ll let you decide for yourself.

You might assume that standard medical advice was supported by mounds of scientific research. But researchers recently discovered that nearly 400 routine practices were flatly contradicted by studies published in leading journals.

 

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