The writing test: should you do it?

math test

When negotiating a project or ongoing work with a new client, copywriters and content marketing writers often get asked to take a writing test. Is it worth your time to write something new in addition to providing writing samples? Sometimes.

A writing test or copy test is a sample exercise some companies ask for so they can get a sense of your writing style and proficiency. It helps companies because they can evaluate multiple writers who are covering the same topic. If it’s a specialized industry; say, B2B technology or health IT, the company may ask you to write on a topic specific to their niche to make sure you know the subject matter. If you’re marketing to companies in your niche, this is no biggie.

Experienced writers grumble at the writing test, myself included. When you have hundreds of clips, including dozens of clips in that client’s niche, those clips should be more than enough to showcase your expertise.

When you’re on a call with a prospective client and your contact says, “…and we have a writing test we’d like you to complete…” do you agree? Or do you say, “Here’s some clips. That’s enough.” Or do you ask them to pay you for the writing test?

I take an IFTTT approach. If this is a potential retainer client or client that would yield ongoing, high-dollar work, and the writing test isn’t that long, I do it. If it’s a client that’s offering so-so pay; they’re a new company that isn’t sure what they want; or it’s a one-off project like a white paper, I’d try to steer them away from the test, pointing to the strength of my existing clips and experience. If the client insists, I evaluate the long-term potential. Is there potential for ongoing work? How much time will this test take me? Can I do it over the weekend?

If the test is long and requires extensive research, and I’m not confident in the future potential of this client, I would decline. If it’s a recruiter that’s asking for the writing test, I’d also decline.

A friend/colleague of mine got approached by a recruiter for potential ongoing work in her specialty. The recruiter asked for a 1,000-word “writing test” that would involve extensive research, evaluating studies, etc. The recruiter wouldn’t tell her where she stood in the hiring process or even the name of the company. At this early stage in the process, she refused the writing test. And she never got a reply from the recruiter.

I think she made a smart move. The recruiter could have been collecting writing test from dozens of writers. Who knows? And who would have evaluated those tests – the recruiter or the client? Too many variables to justify spending a full day on a writing sample that may have went nowhere.

I recently agreed to a couple writing tests. One, a 350-word sample on a specific topic relative to that client’s work. I agreed because I really wanted to work with this client and it would have meant significant, ongoing work. I got the contract and our relationship has been awesome so far.

I agreed on a 500-word writing sample (below) for another prospective client for similar reasons. They had interesting products, were in one of my niches (medtech), and wanted someone to create a substantial amount of content each month. Turns out they hired a full-time in-house copywriter. Bummer. I could say the work was for nothing, but looking at the positives:

• I made two new contacts in one of my niches.

• I asked for referrals, so there’s a chance one of the women I spoke with will make one.

• The experience inspired me to write this post!

Because I have around 600 words that I can’t do much with, I’m posting it here: A sample of a writing test on “Artificial intelligence and clinical trials.”

This was for a healthcare technology company. I had some of the study information from previous articles, which saved a smidgeon of time. I also studied the company’s website to get a good sense of their offerings and how I could tie in the writing test topic to those offerings. The contacts asked me to look at past articles by the CEO to get a sense of style/tone; otherwise, the subject above was all I had to go on.

When do you agree to writing tests? Share your thoughts in the comments below! – Heather J

3 ways AI can transform clinical trials

Artificial intelligence (AI) has transformed how financial services, automotive, and retail industries operate and interact with customers. Even healthcare, a historically slow-moving sector, is finding ways to use AI in research and to gather individual patient data.

Similarly, the pharmaceutical industry has much to gain by adopting AI. More targeted therapies means narrower patient populations, which makes recruitment more challenging and competitive. Increased clinical trial complexity, with more data endpoints, procedures, and eligibility criteria, means longer timelines and, more often than not, costly delays. Meanwhile, only about 13.8% of all drug development programs lead to regulatory approval according to a 2018 study out of MIT.

Where AI steps in

AI and related technologies have the potential to reinvent drug development much like it transformed banking and shopping. From patient recruitment, enrollment, and retention to fraud protection and risk monitoring, AI brings efficiency and speed to clinical trials. Here are a few ways sponsors and/or CROs can leverage the power of new technology.

Patient recruitment

Researchers likely won’t develop another penicillin or insulin in our lifetime. Today, drug developers have to get highly specific in their drug targets. They’re focusing on rare diseases. They’re developing drugs to target people with certain gene mutations or a subtype of a common disease. These new therapies have promise to provide more effective treatment, but studying their safety and efficacy proves challenging at best.

According to a 2017 Tufts survey, 27% of clinical research sites fail to enroll a single patient. About 30% of enrolled patients ultimately drop out. I think we can do better.

AI can extract information from electronic medical records (EMR), including physician’s notes, data from diagnostic images, and other information and rapidly compare it to a study’s inclusion and exclusion criteria. This allows sponsors to access a wider pool of eligible patients more efficiently than manual searches.

Using data from insurance claims, prescription records, and other records, AI can predict which patients are at risk of dropping out. Site staff can then intervene with these patients and develop a plan to mitigate dropout rates.

Risk prevention

Many clinical trials fail to gain regulatory approval due to preventable errors. The FDA conducted a prospective review of drug application submissions between 2000 and 2012 for new molecular entities. Preventable deficiencies, including failure to select optimal drug doses and suitable study end points and inconsistent results between different trials or study sites, accounted for most of the unsuccessful applicants.2 AI can help CROs identify errors during the course of a study, increasing the odds that regulators will accept submissions the first time, avoiding expensive amendments.

Increase site performance

Nothing derails a study more than a rogue site. A site may underperform due to low enrollment numbers, communication issues, or failure to meet timelines.

Platforms using AI can help eliminate duplicative work, increasing the odds a site will hit study timelines. Visualizing patients’ progress at regular intervals helps site staff identify issues and communicate with patients immediately.

AI can also improve subjective patient-reported outcomes data, which often involves ongoing training throughout a study. In 2017, Stanford researchers developed a diagnosis algorithm using 130,000 skin disease images to diagnose potential skin cancer. The algorithm came within 91 percent of matching the diagnosis of 21 dermatologists. In time, we may also have an algorithm to measure surface area or indications associated with eczema, psoriasis, acne, and other conditions.2

Some pharmaceutical companies may hesitate to change processes because of the time, energy, and money involved. But considering the high cost and low success rate in drug development, they can’t afford not to. AI has the potential to help improve clinical trial success rates, ultimately brining more life-saving drugs to more people. I’m excited that XXX’s AI-driven tools have become part of the solution.

1. Sacks LV, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE. Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, 2000-2012. JAMA.2014;311(4):378–384. doi:10.1001/jama.2013.282542

2. Esteva, Andre, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature volume542, pages115–118 (02 February 2017)

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