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College Roundup: Gators Survive, Hurricanes Rally
By Teddy Cahill
How The Top 25 Fared
(1) Florida: won, 8-7, vs. Eastern Michigan at Lakeland, Fla.
(2) Louisville: off
(3) Texas A&M: won, 11-0, vs. Prairie View A&M
(4) Virginia: off
(5) Oregon State: off
(6) Miami: won, 7-6, vs. St. Thomas (Fla.) in 10 innings
(7) Vanderbilt: won, 9-1, vs. Eastern Illinois
(8) Louisiana State: lost, 12-11, at Lamar
(9) Houston: off
(10) Louisiana-Lafayette: lost, 6-2, at Louisiana Tech
(11) California: off
(12) Oregon: off
(13) Oklahoma State: off
(14) Michigan: off
(15) UCLA: off
(16) Florida State: off
(17) Texas Christian: won, 17-3, vs. Arkansas-Pine Bluff
(18) North Carolina State: cancelled vs. St. John's
(19) Coastal Carolina: off
(20) North Carolina: off
(21) Mississippi: won, 4-3, vs. Arkansas State
(22) Kentucky: off
(23) Georgia Tech: won, 6-5, at Georgia Southern in 16 innings
(24) Mississippi State: off
(25) Oklahoma: won, 7-1, vs. Abilene Christian
Trent Grisham Helps Balance The Lineup
The Padres badly needed lefthanded lineup presence, prompting them to trade promising middle infielder Luis Urias to the Brewers for Grisham.
A wild Wednesday night in college baseball was capped off by No. 1 Florida surviving a scare from Eastern Michigan, as the Gators won, 8-7, when Buddy Reed drove in the winning run on a walk-off catcher's interference call. Florida, which improved to 5-0, needed a late comeback after Eastern Michigan scored five runs in the eighth inning to take a 7-4 lead. But the Gators scored twice in each of the next two innings to pull out the victory. Florida's No. 2 ranked recruiting class was integral to the win. Freshman second baseman Deacon Liput (1-for-4, 2 BB, R) hit a two-run triple in the eighth inning, and classmates Jonathan India (2-for-4, 2B, 2 R) and Nelson Maldonado (1-for-1, RBI) both singled in the bottom of the ninth. India scored the winning run. Florida was not the only ranked team to receive a scare Wednesday. No. 6 Miami, which will host Florida in a big series this weekend, needed extra innings to dispatch NAIA St. Thomas (Fla.). The Hurricanes trailed by two runs in the eighth inning before coming back to win, 7-6, in 10 innings. Miami (4-0) stranded 22 runners, breaking a school record that had stood since 1953. No. 23 Georgia Tech (4-0) also needed extra innings before it pulled out a 6-5 victory in 16 innings at Georgia Southern. The Yellow Jackets tied the game at three with a ninth-inning run, but neither team could score again until the 16th inning when Brandon Gold hit a three-run home run to give Georgia Tech the lead. Georgia Southern got two runs back in the bottom half of the inning, but was unable to extend the game further. Freshman righthander Keyton Gibson (2 IP, 1 H, 0 R) earned his first career victory. The game was the longest in Georgia Tech history, surpassing the previous high of 15 innings. While several ranked teams were able to stave off upsets, No. 8 Louisiana State was not so fortunate. LSU took an 8-0 lead in the second inning at Lamar, but the Cardinals overcame the deficit and held on for a 12-11 victory. Video The game held added intrigue, as former LSU assistant coach Will Davis has been tapped to replace longtime Lamar coach Jim Gilligan when he retires at the end of the season. Following the game, Davis told Chandler Rome of the Baton Rouge Advocate that it was one of the greatest games he'd ever been a part of, regardless of the opponent. "My heart bleeds for them," Davis said. "I love the guys in that purple and gold dugout and I love that coaching staff and they're going to be fine." LSU fell to 3-1 with the loss, while Lamar improved to 5-0. It is the Cardinals best start to a season since 2010, which was also the last time Lamar went to the NCAA Tournament.
Injury Truncates Breakout Season From Jorge Ona
The 23-year-old Cuban corner outfielder had begun to show elevated power and patience before an injury ended his season.
Baseball America Prospect Report -- July 11, 2019
Royce Lewis and Travis Swaggerty continue their hot hitting in today's prospect report.
Baseball America Prospect Report -- May 14, 2019
A pair of Rockies infielders make today's prospect report, Jarren Duran keeps hitting, Buddy Reed flirts with a cycle and more.


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Kevin Feige And Disney Might Be Arguing Over R-Rated Content! Deadpool Is Safe, Though!
Published on August 11th, 2021 | Updated on August 11th, 2021 | By FanFest
It sounds like Kevin Feige and Disney might be arguing over R-rated content in the MCU. This makes sense, based on everything we know when it comes to Disney. It sounds like, behind the scenes, it was a struggle to get them to even approve of Deadpool. Luckily Deadpool is safe, and the third film will keep its R rating.
What about the rest of the MCU shows, though? Some really need that R rating. Well, need might be a bit of a stretch, but, it certainly helps and fits with the tone. Can you imagine a PG-13 Punisher? I don't know how well that would work.
Part of the appeal of the Netflix Marvel shows was how dark and adult they were. There is certainly a case to be made that that kind of content can't work for the MCU. After all, people loved the Netflix shows. They're even begging for more!
If you look at the last ten years, too, you'll see R-rated superhero films have been killing it. From Deadpool to Logan, both were incredibly successful in their own right. Deadpool especially thanks to Ryan Reynolds.
GiantFrakinRobot is reporting that Feige is having difficulties adhering to the PG-13 rating outside of Deadpool. In fact, it sounds like some ideas were even pitched to have an R rating and Disney just wasn't having it.
This is incredibly frustrating when you consider the fact that Disney recently acquired Fox. They put a ton of more mature content on Hulu and in Canada that content is even available directly on Disney+ through Star.
So, what's going to happen? Well, we're not 100% sure right now. The fact that Deadpool is safe is all that really matters right now. Hopefully, some kind of arrangement can be made since we want the Netflix characters back.
Do you think Kevin Feige and Disney are arguing over R-rated content? Let us know in the comments.
Alexander Ouellet
Alexander is passionate about everything related to gaming, comics, television, and film. You can usually find him watching some kind of DC show and can also find his writing on Multiversal News, something he's helping to build.
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March 27, 2014, 9:56 a.m.
Access to broadband could be the universal catalyst that lifts developing countries out of poverty and puts access to health care, education and basic social services within reach of all, according to the UN Broadband Commission for Digital Development, which met in Dublin recently.
The Commission reiterated its call to International community to recognize the transformational potential of high-speed networks and ensure broadband penetration targets are specifically included in the UN post-2015 Sustainable Development Goals.
It also urged governments and international financing bodies to work to remove current barriers to investment. Globally, as much as 95% of telecommunications infrastructure is private sector-funded, but better incentives are urgently needed if investment is to expand in line with the coming exponential growth of connected users and so-called 'Internet of Things' data streams.
In the world's 200 biggest cities, the number of connected devices is forecast to increase from an average of 400 devices per square kilometre to over 13,000 devices per square kilometre by 2016.
The Commission, which includes some of the world's most prominent leaders from the tech sector, government, academia and UN agencies. gathered in Dublin at the invitation of Denis O'Brien, Chairman of the Digicel Group and one of the founding members of the group.
Established in 2010, the Commission is a top-level advocacy body which focuses on strategies to make broadband more available and affordable worldwide, with a particular emphasis on accelerating progress towards the eight UN Millennium Development Goals.
It is chaired by President Paul Kagame of Rwanda and Mexico's Carlos Slim Helú, with ITU Secretary-General Dr Hamadoun I. Touré and UNESCO Director-General Irina Bokova as co-Vice Chairs.
"The long-sought panacea to human poverty may at last be within our reach in the form of broadband networks that empower all countries to take their place in the global economy, overcoming traditional barriers like geography, language and resource constraints," said O'Brien, whose companies provide mobile services in some of the world's most challenging environments and disadvantaged countries, such as Haiti and Papua New Guinea.
To drive faster broadband roll-out, O'Brien called on governments to lower spectrum license fees and advocated for the establishment of a 'champion's league' index that tracks best practice in broadband investment and deployment.
In his welcoming remarks, Rwanda's President Kagame noted that broadband and ICTs can deliver more efficiency in education, health, finance, banking and other sectors. "In Rwanda, the broadband model we have adopted is based on effective public private partnership, guided by what works on the ground," he said. "This has allowed broadband and ICT to continue to play an important role in the progress we have made towards the achievement of the Millennium Development Goals." Rwanda is currently rolling out a nationwide 4G mobile broadband network through a public private partnership.
Uptake of ICT is accelerating worldwide, with mobile broadband recognized as the fastest growing technology in human history. The number of mobile phone subscriptions now roughly equals the world's total population of around seven billion, while over 2.7 billion people are online. Active mobile broadband subscriptions now exceed 2.1 billion – three times higher than the 700 million wireline broadband connections worldwide.
Most encouragingly, most of this progress has taken place in the developing world, which has accounted for 90% of global net additions for mobile cellular and 82% of global net additions of new Internet users since early 2010, when the Commission was set up.
"That translates to 820 million new Internet users and two billion new mobile broadband subscribers in developing countries in just four years," said ITU Secretary-General Dr Hamadoun I. Touré, who urged Commissioners to consider defining a Broadband for MDGs Acceleration Framework which could be presented for endorsement to the UN Secretary-General at the next meeting of the Commission in New York in September, ahead of the UN General Assembly. "For the first time in history, broadband gives us the power to end extreme poverty and put our planet on a new, sustainable development course," he said.
In 2011, the Commission set four ambitious broadband policy and access targets. The seventh meeting of the Commission, held in Mexico City in March 2013, added a fifth target mandating 'gender equality in broadband access by the year 2020', aimed at redressing gender imbalances in access to information and communication technologies.
In addition to broadband and UN sustainable development goals, the agenda of the Dublin meeting covered the changing role of telecom operators and content providers, and innovative solutions for rolling out rural broadband. The gathering also included a meeting of the newly-formed Working Group on Financing and Investment, held on Saturday 22 March.
At that meeting, Commissioners discussed the urgent need for new strategies to finance the massive new investment in telecoms networks needed to cope with a forecast huge surge in mobile data volumes.
Last September at the eighth meeting of the Commission in New York, the group released the second edition of its global snapshot of broadband deployment, entitled The State of Broadband 2013 Universalizing Broadband, featuring country-by-country rankings based on access and affordability. In that report, Ireland ranked 35th out of 183 economies for fixed broadband access, 19th out of 170 economies for mobile broadband access, and 31 out of 192 economies in terms of percentage of inhabitants using the Internet (79%).


Tours Arenal is a division of Agencia Destino Tropical, a travel agent and tour operator located in La Fortuna de San Carlos, Costa Rica.
This is a family project run by my wife Karla Picado and myself Mario Cordero. Both of us have worked long years in the tourism industry; first in hotels, later as white water rafting and bike tour guides in Costa Rica and Europe. More recently as country coordinators for an international tour operator.
In 2002 we got engaged and decided we did not want to live in San Jose anymore. The quality of life in the city was unbearable for these two runners, bikers, birdwatchers, and all around hyperactive souls. We looked for a better place all over the country, and then looked again. La Fortuna, at the base of Arenal Volcano, was the ideal location, with lots of good roads for biking and running, rainforests for bird watching and a thirving tourism industry. We also thought this was a safe and quiet place to raise a family.
We are both Licensed Costa Rica Naturalist Guides by the National Institute of Biodiversity (INBIO). We have organized and guided many tours for groups, couples, families, students and many bikers.
Our goal is to help you plan your visit to Arenal Volcano in Costa Rica, with accurate information and an efficient booking service. We provide a curated list of the best tours and activities in the area, with clear and updated descriptions and a great deal of local experience.
This site is just beginning and perhaps the information you need is still not here. So we ask you to please write to us. We still respond to all email on the same day and we'll answer all of your questions. If we don't have an answer, I'm sure we know where to find it. And if you are ever visiting Arenal Volcano and La Fortuna, we'd love say hello and show you around.


I've just published the first plug-in tool for Comicster 4. This will merge storylines and autographs from the CMX file you originally converted from the previous version of Comicster.
Note that it finds storylines and issues based on the internal IDs, so if you've removed an issue and added it back in since you opened the CMX file originally, this merge tool won't find it.
1. Run Comicster and make sure it updates to the very latest version.
2. Download the zip file from here and extract the two DLLs inside to your "Comicster" folder under "My Documents". One of them (Comicster.Data.dll) is probably already in there, but it's safe to overwrite.
This is meant to be a temporary thing so I haven't put any effort into a nice user interface for it. Give it a whirl and let me know how it goes. I'll update the tool later when I introduce a "Printing" field for issues, and blog about it here.
A feature that was in the previous version that I'd forgotten about in v4 (thanks for the reminder, John) was the ability to record whether a creator has signed an issue. That's now available in 4.0.0.11.
Now, if you've already imported your old CMX file and you're missing some features that I've added in the past few versions (like autographs or storylines), let me know in the comments on this post if you'd like to see a plug-in tool to import those from your original file.
With the latest release of Halfwit (version 2.0.0.36) I've changed the way the "Reply" and "Reply to All" actions work.
Having two different behaviours depending on the shift key (no pun intended, Brendan) was a bit weird, so I've rolled the two up in what I hope is an intuitive combination.
That means you can just start typing if you only want to reply to the original tweeter, and the other usernames will get replaced. If you want to reply to all, you can press Home or End on your keyboard to deselect the text and then type your reply.
I think this is a nicer balance between "Reply" and "Reply to All". It'll take some getting used to though. Let me know what you think.
You can easily add a new storyline by simply typing its name into that field – it'll get created and show up in the list alongside all the others in your collection.
Just like in the previous version I'm deliberately limiting it to one storyline per issue. I don't think that'll be too severe a limitation – certainly it has never bothered me.
For those of you with lots of crossover events in your old .CMX file, you'll find an updated CMX file plug-in on for download on the Bitbucket site.
One reason both Mad Props and Matt Hamilton.NET have been a bit quiet lately is that Sal and I have been in the US and Canada so that I could attend the 2011 MVP Global Summit at Microsoft.
We spent six nights in Vancouver before the summit, which aptly demonstrated why that city is often voted the "most liveable city" in the world. In fact, it received the award for 2011 while we were there. Despite unseasonably cold weather (one day only managed to get as high as -2°C) we thoroughly enjoyed our stay. Vancouver served up some of the most amazing meals I've ever had, including a five-course monster at Tojo's, the restaurant owned and run by the inventor of the "California Roll". No Japanese food is going to taste the same after that.
Once our stay in Vancouver was over, we took a coach to Seattle and then a bus out to Bellevue where we'd be staying for the next five nights during the summit. Bellevue is home to a large shopping mall called Bellevue Square, which is where Sal would find herself for the next four days while I visited Microsoft campus.
The summit itself was somewhat mind-blowing. The point of the summit, as I understand it, is to give MVPs a chance to meet with the product teams and influence the next version of the products in which they specialise. My MVP area of expertise is "client application development" so I got to meet the WPF team and sit in a day and a half of sessions focusing on WPF. It was fascinating to sit in a room full of people who are truly experts in WPF, whose complaints and criticisms stem from a deep understanding of the framework and from trying to do some really advanced things. For this little line-of-business application developer, it was quite humbling.
Better than the content, though, was the "networking" that came along with so many passionate geeks being in the same place at the same time. Half a world away from home, I met a whole bunch of fellow Aussie developers whom I had only conversed with online until now. On top of that, I got to meet some of my idols from Microsoft – people like Glenn Block, Jon Galloway, Scott Hanselman, Pete Brown, Rob Relyea, Phil Haack, Damian Edwards and the red-polo himself, Scott Guthrie. Then there were the other MVPs who I interact with via Twitter that I finally got to meet in person – Laurent Bugnion, Rob Eisenberg, Richard Campbell, John Sheehan – the list goes on and on (and if I've left you out, it wasn't deliberate – I apologise)!
Following the summit, Sal and I shifted to Seattle proper where we spent five nights at Inn at the Market, right in the heart of the city. Unfortunately I'd picked up a rather nasty chest infection (as did several other summit attendees, apparently) so we didn't get to do as many touristy things in Seattle as we would have liked. It was still a fun week, though, with lots of good food and shopping to be had.
Edit: Ooh! I forgot to mention that we got to go to Emerald City Comicon while we were in Seattle! We went to a Guild discussion panel with Felicia Day, Will Wheaton and Amy Okuda, and I got a personally-signed comic from Kurt Busiek! We had no idea that ECCC was on while we were there – just a lucky accident.
Overall the 2.5 weeks we were away made for what might have been our best vacation yet. If I'm re-awarded as an MVP again this year, I can't wait to go back next year and reconnect with everybody!
Last night I decided to try something in Comicster that ended up working really well, and will make it to the next release.
This new approach lets you add from multiple groups, or copy from multiple issues, without having to go in and out of a dialog box. It's a lot slicker and feels much more "tightly integrated" with the rest of the interface.
Look for this in the next release of Comicster. The next major feature will be the addition of "storylines" which can be defined centrally and shared across issues (great for major events or crossovers). Don't know how long that one will take (especially since I'll be overseas for three weeks) but I'm looking forward to having a crack at it!
I've just published Comicster 4.0.0.6, which adds the ability to copy the credits and appearances from another issue in the same title. Here's a screenshot!
Comicster 1.x had this feature too, but it was a two step process – you had to copy the credits and appearances separately. I've combined the two into one handy dialog in this new version!
I've just published version 4.0.0.3 of Comicster, which adds the final piece of the "group" puzzle: the ability to add appearances to an issue from a previously defined group. Here's a screenshot! You know the drill.
Comicster 4 is still very much a work in progress, but it's at least as feature-rich as the old version now – just a few finishing touches and I'll be happy to call it "out of beta".
As always, if you can think of things you'd like to see, or things that need fixing, log an issue over on BitBucket.
Comicster 4.0.0.2 is now available, with limited support for groups/teams. Comicster will automatically update when you next launch it.
The idea is that you can define your own groups and add characters to them, and then eventually it'll be easy to add appearances to an issue by choosing a group and then selecting which members of that group appeared.
Pro-tip: Double-clicking on a character in this view (in the middle column) will jump you straight to that character's entry in the "characters" view, so you can view the issues they appeared in etc.
The Comicster.IO.Cmx plug-in, which allows you to open your Comicster 1.x collection, has been updated and will now pull your existing groups in. Grab the latest version of the plug-in and install it by following the instructions in the previous post.
Ok, there's a bit of demand out there for this plug-in, so I've made it available via BitBucket until such time as I can get an "official" plug-in page up on the Comicster web site.
The Comicster.IO.Cmx plug-in allows Comicster 4 to open Comicster 1.x (I guess technically it's 2.x, but whatever) files. It doesn't support saving, so you'll need to save your files in Comicster 4's new .cmxx file format.
That's it! Comicster will look for plug-ins in a folder called "Comicster" under your "My Documents" folder. The Comicster.IO.Cmx plug-in contains two files – one is the plug-in itself, and the other is a "helper" file that tells the plug-in how to load .cmx files.
In a future version I think I'll probably make Comicster look in a special "Comicster\Extensions" subfolder rather than just "Comicster" (since you'll probably want to keep your collection files in there and we don't want that folder getting too full), so keep an eye out for that change. I'll make sure I announce it here.


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Articles| Volume 379, ISSUE 9823, P1310-1319, April 07, 2012
A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis
Prof Anthony J Avery, MD
Anthony J Avery
Correspondence to: Prof Anthony J Avery, Division of Primary Care, University of Nottingham Medical School, Queen's Medical Centre, Nottingham, NG7 2UH, UK
Division of Primary Care, University of Nottingham Medical School, Queen's Medical Centre, Nottingham, UK
Sarah Rodgers, PhD
Sarah Rodgers
Division of Primary Care, NIHR Collaboration for Leadership in Applied Health Research and Care—Nottinghamshire, Derbyshire & Lincolnshire
Prof Judith A Cantrill, MSc
Judith A Cantrill
Drug Usage & Pharmacy Practice Group, School of Pharmacy & Pharmaceutical Sciences, University of Manchester, Manchester, UK
Sarah Armstrong, PhD
Sarah Armstrong
The NIHR Research Design Service, Division of Primary Care, Nottingham, UK
Kathrin Cresswell, PhD
Kathrin Cresswell
eHealth Research Group, Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK
Martin Eden, MRes
Martin Eden
Prof Rachel A Elliott, PhD
Rachel A Elliott
Division for Social Research in Medicines and Health, The School of Pharmacy, University of Nottingham, Nottingham, UK
Rachel Howard, PhD
Rachel Howard
School of Pharmacy, University of Reading, Reading, UK
Prof Denise Kendrick, DM
Denise Kendrick
Caroline J Morris, PhD
Caroline J Morris
Department of Primary Health Care and General Practice, Wellington School of Medicine and Health Sciences, University of Otago, Wellington South, New Zealand
Prof Robin J Prescott, PhD
Robin J Prescott
Glen Swanwick
Consumers in Research Advisory Group, Research and Evaluation Team, Quality and Governance Directorate, NHS Nottinghamshire County, Mansfield, Nottinghamshire, UK
Matthew Franklin, MSc
Matthew Franklin
Koen Putman, PhD
Koen Putman
Department of Medical Sociology and Health Sciences, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium
Matthew Boyd, PhD
Matthew Boyd
Prof Aziz Sheikh, PhD
Aziz Sheikh
Open AccessPublished:February 21, 2012DOI:https://doi.org/10.1016/S0140-6736(11)61817-5
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Medication errors are common in primary care and are associated with considerable risk of patient harm. We tested whether a pharmacist-led, information technology-based intervention was more effective than simple feedback in reducing the number of patients at risk of measures related to hazardous prescribing and inadequate blood-test monitoring of medicines 6 months after the intervention.
In this pragmatic, cluster randomised trial general practices in the UK were stratified by research site and list size, and randomly assigned by a web-based randomisation service in block sizes of two or four to one of two groups. The practices were allocated to either computer-generated simple feedback for at-risk patients (control) or a pharmacist-led information technology intervention (PINCER), composed of feedback, educational outreach, and dedicated support. The allocation was masked to researchers and statisticians involved in processing and analysing the data. The allocation was not masked to general practices, pharmacists, patients, or researchers who visited practices to extract data. Primary outcomes were the proportions of patients at 6 months after the intervention who had had any of three clinically important errors: non-selective non-steroidal anti-inflammatory drugs (NSAIDs) prescribed to those with a history of peptic ulcer without co-prescription of a proton-pump inhibitor; β blockers prescribed to those with a history of asthma; long-term prescription of angiotensin converting enzyme (ACE) inhibitor or loop diuretics to those 75 years or older without assessment of urea and electrolytes in the preceding 15 months. The cost per error avoided was estimated by incremental cost-effectiveness analysis. This study is registered with Controlled-Trials.com, number ISRCTN21785299.
72 general practices with a combined list size of 480 942 patients were randomised. At 6 months' follow-up, patients in the PINCER group were significantly less likely to have been prescribed a non-selective NSAID if they had a history of peptic ulcer without gastroprotection (OR 0·58, 95% CI 0·38–0·89); a β blocker if they had asthma (0·73, 0·58–0·91); or an ACE inhibitor or loop diuretic without appropriate monitoring (0·51, 0·34–0·78). PINCER has a 95% probability of being cost effective if the decision-maker's ceiling willingness to pay reaches £75 per error avoided at 6 months.
The PINCER intervention is an effective method for reducing a range of medication errors in general practices with computerised clinical records.
Patient Safety Research Portfolio, Department of Health, England.
Medication errors are an important cause of potentially avoidable morbidity and mortality in primary
Gandhi TK
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Borus J
Adverse drug events in ambulatory care.
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Harrold LR
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and secondary care
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Laird N
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and reports from the USA, the UK, and elsewhere have shown the urgent need to reduce the risk of occurrence of these errors.
An organisation with a memory: report of an expert group on learning from adverse events in the NHS. Department of Health, London2000
Kohn L
Corrigan J
Donaldson M
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Smith J
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Although important progress has been made in the implementation of interventions for use in specialist care settings,
Gawande AA
Improving safety with information technology.
particularly in relation to computerised entry of physician orders
Schedlbauer A
Prasad V
Mulvaney C
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J Am Med Inform Assoc. 2009; 16: 531-538
and computerised decision support,
Garg AX
Adhikari NK
McDonald H
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the evidence for primary care—in which most patients are now managed worldwide—is still very weak.
Smeaton L
Avery AJ
Hurwitz B
Sheikh A
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On the basis of systematic reviews of published work
Howard RL
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and our own research,
Howard PD
Partridge M
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Qual Saf Health Care. 2003; 12: 280-285
Chen YF
Neil KE
Johnson C
Dewey ME
Stockley IH
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we identified the drugs most commonly associated with medication errors in primary care.
In view of the few known effective interventions, we focused on the identification of the most promising components of any future intervention.
The evidence was strongest for educational outreach
Thomson O'Brien MA
Oxman AD
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Haynes RB
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and pharmacist-led interventions.
Furthermore, most preventable adverse drug events in primary care are attributable to errors in prescription and medication monitoring,
and changes in practice enabled by information technology have substantial potential to reduce the frequency of these errors.
However, translation of this potential into proven benefits is far from straightforward, which relates to the difficulties in making the organisational changes needed to embed information technology into routine models of care.
Catwell L
Evaluating eHealth interventions: the need for continuous systemic evaluation.
PLoS Med. 2009; 6: e1000126
The need for a new multifaceted intervention has been further underscored by two trials that have raised serious doubts about the effectiveness of simple pharmacist-centred interventions.
Richmond S
Morton V
Cross B
RESPECT Trial Team
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Holland R
Lenaghan E
Harvey I
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Informed by the Medical Research Council's framework for complex interventions,
A framework for the development and evaluation of RCT's for complex interventions. Medical Research Council, London2000
we aimed to test whether an information technology intervention for pharmacists could improve prescription safety and medication monitoring in general practices. We also undertook an indicative analysis of the cost-effectiveness of the intervention.
We did a two-group pragmatic cluster randomised trial. Further details of the methods are available in the trial protocol.
Rodgers S
Cantrill JA
Protocol for the PINCER trial: a cluster randomised trial comparing the effectiveness of a pharmacist-led IT-based intervention with simple feedback in reducing rates of clinically important errors in medicines management in general practices.
Trials. 2009; 10: 28
We chose a cluster design because the intervention was applied by the general practice. General practices were eligible to participate if they were computerised with electronic prescribing. Practices were excluded if they did not routinely record morbidities such as asthma or peptic ulcer in patients' computerised records; did not routinely use computers to record prescriptions issued; intended to change their computer systems during the study to that of a different supplier that was not compatible with Quest Browser; were in primary care trusts that were undertaking interventions that might overlap with our intervention; took part in the pilot study of the trial; or expected large changes in list-size (numbers of registered patients) during the study.
The study was approved by the Nottingham 2 research ethics committee. We obtained written consent from all general practices after a face-to-face meeting at which the study was explained in more detail. For the economic analysis, the general practices recruited to the study were asked to write to all patients identified through baseline data collection who appeared in the numerator of one of our outcome measures (ie, they had a potential medication error). Patients were given a leaflet about the study and were asked to give written consent for the research team to access their medical records.
Randomisation and masking
We stratified eligible practices by centre (Manchester and Nottingham) and list size (<2500, 2500–6000, and >6000 patients) and randomly allocated them within strata (1:1 ratio) to receive the intervention or control by block randomisation with block sizes of two or four. The allocation sequence was centrally generated, independent of the investigators, by the clinical trials unit at the University of Nottingham (UK). Practices were enrolled by the research team and allocated by the independent web-based randomisation service provided by the unit. Access to the allocation sequence was restricted to the clinical trials unit data manager and the sequence was concealed until all analyses were completed.
General practices, patients, pharmacists, and researchers visiting practices to extract computerised data were not masked to the allocation. All outcome data for the trial were extracted from patient records by prespecified electronic searches and so these data could not be altered before leaving the practice. Data were then sent electronically by secure file transfer protocol and automatically entered into a database on the trial manager's computer. Researchers who cleaned the data and the trial statisticians were masked to allocation.
Because patients were proactively identified as being at high risk of potentially serious errors, we judged that a control group of no intervention was unethical. The control group practices therefore used simple feedback; after collection of data at baseline, control practices received computerised feedback for patients identified as at risk from potentially hazardous prescripting and inadequate blood-test monitoring of medicines plus brief written educational materials explaining the importance of each type of error. Practices were asked to introduce changes they considered necessary within 12 weeks after the collection of data at baseline.
Intervention practices received simple feedback plus a pharmacist-led information technology complex intervention (PINCER) lasting 12 weeks. The pharmacist arranged to meet with members of the practice team to discuss the computer-generated feedback for patients with medication errors. All doctors were encouraged to attend this meeting with at least one member of the nursing staff, the practice manager, and at least one member of the reception staff. Before the meeting, whenever possible, all relevant members of staff were provided with a brief summary of the objectives of the PINCER intervention and a summary of the findings from the computer search. At the meeting the pharmacists were asked to use the principles of educational outreach while taking account of human error theory in their discussions. After this initial meeting, pharmacists were expected to use various techniques to help correct medication errors that had been identified and prevent future medication errors. Interventions included review of patients' medical records; discussions with family doctors to decide on actions to be taken; invitation of patients to be reviewed or to have blood tests; and working with members of the practice team to improve local safety systems. Details are provided in the trial protocol.
All general practices had access to some computerised decision support, such as drug interactions, for prescription.
Data were extracted at baseline and at 6 months and 12 months after the intervention with Quest Browser software (version 2.3.39).
We chose our primary and secondary outcomes on the basis of medicines management difficulties that are important in overall burden and severity of iatrogenic harm in primary care,
and those that are detectable from general practice computer systems. The three primary outcomes assessed after 6 months were (1) prescription of non-selective non-steroidal anti-inflammatory drugs (NSAIDs) to patients with a history of peptic ulcer without co-prescription of a proton-pump inhibitor (PPI); (2) prescription of β blockers to patients with a history of asthma; and (3) long-term prescription of angiotensin converting enzyme (ACE) inhibitors or loop diuretics to patients 75 years or older without assessment of urea and electrolytes in the preceding 15 months. Panel 1 shows the primary, secondary, and composite outcomes (see also the trial protocol
Patients with a history of peptic ulcer who have been prescribed a non-selective non-steroidal anti-inflammatory drug without co-prescription of a proton-pump inhibitor
Patients with asthma who have been prescribed a β blocker
Patients aged 75 years and older who have been prescribed an angiotensin converting enzyme inhibitor or a loop diuretic long-term who have not had a computer-recorded check of their renal function and electrolytes in the previous 15 months
Patients with asthma (and no history of coronary heart disease) who had been prescribed a β blocker
Proportions of women with a past medical history of venous or arterial thrombosis who had been prescribed the combined oral contraceptive pill
Patients receiving methotrexate for at least 3 months who had not had a full blood count recorded (5a), or liver function test (5b), in the previous 3 months
Patients receiving warfarin for at least 3 months who had not had a recorded check of their international normalised ratio in the previous 12 weeks
Patients receiving lithium for at least 3 months who had not had a recorded check of their lithium concentrations in the previous 3 months
Patients receiving amiodarone for at least 6 months who had not had a thyroid function test in the previous 6 months
Patients receiving prescriptions of methotrexate without instructions that the drug should be taken every week
Patients receiving prescriptions of amiodarone for at least 1 month who are receiving a dose of more than 200 mg per day
Composite secondary outcome measures
Patients with at least one prescription problem (a combination of outcome measures 1, 2, or 4)
Patients with at least one monitoring problem (a combination of outcome measures 3, 5, 6, 7, and 8)
In the trial protocol
composite outcome measures for patients with two or more prescription or monitoring problems were suggested. However, the number of patients with two or more problems was very small and so these findings are not reported in this Article.
Characteristics of practices and participants were recorded at baseline and compared informally between treatment groups.
The prevalences of primary and secondary outcomes were measured at each timepoint by allocation group, at the level of the patient, using the numerator, denominator and percentage.
Analysis was by intention to treat.
Hollis S
Campbell F
What is meant by intention to treat analysis? Survey of published randomised controlled trials.
BMJ. 1999; 319: 670-674
Shih WJ
Problems in dealing with missing data and informative censoring in clinical trials.
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All outcome measures were binary and were compared between groups by random effects logistic regression, with patient at level one and practice at level two, to estimate odds ratios and 95% CIs. The primary analysis adjusted for stratum as a fixed effect (practice level), errors related to medication at baseline (patient level), deprivation,
Department for Communities and Local Government
Indices of Deprivation 2004.
http://webarchive.nationalarchives.gov.uk/+/http://www.communities.gov.uk/archived/general-content/communities/indicesofdeprivation/216309/
and training status (practice level). We estimated intraclass correlation coefficients from these models.
Subgroup analyses for primary outcome measures assessed whether the intervention effect varied by practice size or practice deprivation by incorporation of a term for the interaction between treatment group and the (continuous) covariate of interest into regression models.
Assmann SF
Pocock SJ
Enos LE
Kasten LE
Subgroup analysis and other (mis)uses of baseline data in clinical trials.
We categorised the covariate at the median value when there was evidence of non-linearity.
Significance was assessed with likelihood ratio tests with a p value of less than 0·05. All analyses were done with Stata (version 10.1).
StataCorp LP
Stata data analysis and statistical software. Special Edition Release. 10.1 ed. Stata, College Station2008
We obtained outcome data for all participating practices at both the 6 months (primary) and 12 months assessment points, no data were missing. No adjustments were made for multiple endpoints. We checked models by examining plots of standardised empirical Bayes estimates for the random effects and did sensitivity analyses by excluding practices with estimates above or below two standard deviations.
We calculated sample sizes separately for all primary outcomes. To calculate sample sizes unadjusted for clustering we used nQuery Advisor (version 6.0),
Elashoff JD. nQuery Advisor®. Version 6.0 software. Los Angeles; 2005.
and to inflate to adjust for clustering
Kerry SM
Bland JM
Statistics notes: the intracluster correlation coefficient in cluster randomisation.
we used intraclass correlation coefficients and average cluster sizes estimated from 43 general practices that contributed anonymous clinical data to the QResearch database (webappendix).
66 practices were needed to detect a difference between an 11% reduction in error rate in the simple feedback group and a 50% reduction in the PINCER group for the primary outcome measures with 80% power and a two-tailed α of 0·05.
In a prospective economic evaluation (webappendix), we compared PINCER with simple feedback for reduction of proportions of patients at risk from prescription errors in general practice, from the perspective of the UK NHS. Costs and outcomes associated with sequelae of errors were not included.
The outcome for the economic analysis was the number of errors detected in the PINCER and simple feedback groups at 6 months and 12 months after the intervention. We used outcome measures 1, 2, 3, 5a, 5b, 7, and 8 (table 1). We excluded outcome measure 4 because the number of patients with errors was very small and outcome measures 6, 9, and 10 because obtaining full data in all practices was difficult.
Table 1Characteristics of patients at baseline by allocation group according to outcome measure
Simple feedback
1 History of peptic ulcer prescribed an NSAID without a PPI/history of peptic ulcer without a PPI 93/1970 (5%) 87/1828 (5%)
2 Asthma prescribed a β blocker/asthma 628/20 634 (3%) 537/18 906 (3%)
3 Aged ≥75 years receiving long term ACE inhibitors or loop diuretics without urea and electrolyte monitoring in the previous 15 months/aged ≥75 years receiving long-term ACE inhibitors or diuretics 483/4722 (10%) 549/4349 (13%)
2a Asthma and not CHD prescribed a β blocker/asthma and not CHD 375/19 528 (2%) 337/17 968 (2%)
4 History of venous or arterial thrombosis prescribed combined oral contraceptives/history of venous or arterial thrombosis (women) 16/2588 (1%) 5/2284 (<1%)
5a Methotrexate for ≥3 months without full blood count in past 3 months/methotrexate for ≥3 months 202/483 (42%) 170/480 (35%)
5b Methotrexate for ≥3 months without a liver function test in past 3 months/methotrexate for ≥3 months 184/483 (38%) 172/480 (36%)
6 Warfarin for ≥3 months without an INR in past 3 months/warfarin for ≥3 months 99/1496 (7%) 92/1591 (6%)
7 Lithium for ≥3 months without a lithium concentration measurement in past 3 months/lithium for ≥3 months 101/224 (45%) 97/194 (50%)
8 Amiodarone for ≥6 months without a thyroid function test in the past 6 months/amiodarone for ≥6 months 130/253 (51%) 111/240 (46%)
9 Methotrexate without instructions to take weekly/patients prescribed methotrexate 12/345 (3%) 7/305 (2%)
10 Amiodarone for ≥1 month at a dose of >200 mg per day/amiodarone for ≥1 month 1/223 (<1%) 1/222 (<1%)
11 At least one prescription problem/at risk of at least one prescription problem 736/24 550 (3%) 629/22 473 (3%)
12 At least one monitoring problem/at risk of at least one monitoring problem 1015/6756 (15%) 1018/6371 (16%)
Data are numerator/denominator (%). NSAID=non-steroidal anti-inflammatory drug. PINCER=pharmacist-led information technology intervention. ACE=angiotensin converting enzyme. PPI=proton-pump inhibitor. CHD=coronary heart disease. INR=international normalised ratio.
Costs were the direct costs of provision of an intervention to reduce prescription errors in general practice. The study was not powered to detect differences in costs because no previous study on which to base a power calculation exists. The time horizon was 6 months in the base case. Because all costs were incurred by the practices, correction for clustering was not needed.
The only cost for the simple feedback group was from the researchers returning to the practices at set times to generate error reports from general practice systems. This cost was retained in the model to account for the equivalent resource that would be used to generate these error reports in the real world. The PINCER intervention consisted of these report-generation costs, plus pharmacist training sessions, facilitated meetings, monthly meetings, practice feedback meetings, and time spent in each practice outside meetings following up errors.
Before incremental cost-effectiveness analysis, we adjusted costs and outcomes for a range of practice characteristics. We used the negative binomial model
Hilbe JM
Negative binomial regression. Cambridge University Press, New York2007
for regression analysis of errors because variance was greater than the mean for errors per practice in both groups (overdispersion), and the variation differed between groups. Poisson regression would underestimate the standard errors of the coefficients. We estimated adjusted costs by generalised linear modelling. A simple probabilistic decision-analytic model was populated to generate incremental cost-effectiveness ratios, which were calculated for differences in error rates between the PINCER and simple feedback groups:
(cost PINCER-cost simple feedback)(outcome PINCER-outcome simple feedback)
To identify the magnitude of uncertainty of the incremental cost-effectiveness ratios, we generated a bootstrap estimate of the incremental cost-effectiveness ratio sampling distribution. We constructed a cost-effectiveness acceptability curve to express the probability that the cost per extra unit of outcome (error avoided in this study) gained from the trial was cost effective as a function of the decision-maker's ceiling cost-effectiveness ratio (λ). We did a sensitivity analysis to establish incremental cost-effectiveness ratios when the time horizon was 12 months. A detailed description of the analysis is provided in the trial protocol.
This study is registered, number ISRCTN21785299.
Role of the funding source
The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
240 practices in Nottinghamshire, Staffordshire, and central and eastern Cheshire, England, were invited to participate, of which 72 (30%) were recruited between July 11, 2006, and Aug 8, 2007 (figure 1). Participating and non-participating practices had much the same numbers of family doctors and socioeconomic profiles, however, participating practices were larger, more likely to be training practices, and had slightly higher quality and outcomes framework scores, than did non-participating practices (webappendix). Table 2 shows the number of patients per practice with each primary outcome measure. The main reason practices gave for not taking part was that they were too busy. In the simple feedback group, three practices had a small list size, 14 a medium list size, and 19 a large list size. In the PINCER group, four practices had a small list size, 13 a medium list size, and 19 a large list size. Table 1 shows participant charactersistics and the webappendix shows practice characteristics at baseline for the two treatment groups.
Figure 1Trial profile
*Repeated cross-sectional design accounts for no loss to follow-up of patients. PINCER=pharmacist-led information technology-based intervention.
Table 2Mean number of patients per practice for each primary outcome measure
At baseline
At 6 months
At 12 months
Outcome 1 55 (7–129) 51 (9–124) 56 (10–130) 51 (11–120) 56 (12–136) 51 (12–115)
Outcome 2 573 (93–1215) 525 (94–1356) 617 (106–1307) 564 (104–1438) 653 (111–1381) 593 (118–1499)
Outcome 3 131 (22–379) 121 (18–287) 148 (26–462) 135 (21–331) 161 (30–492) 146 (22–365)
Data are mean (range). PINCER=pharmacist-led information technology intervention.
Table 3 shows results for the primary and secondary outcome measures at 6 months. For the primary outcomes, participants in the PINCER group were significantly less likely to have been prescribed a NSAID without a PPI if they had a history of peptic ulcer, a β blocker if they had asthma, or an ACE inhibitor or diuretic without having had urea and electrolytes measured in the preceding 15 months (table 3). Treatment group and practice size or deprivation did not interact significantly (data not shown). The intraclass correlation coefficients estimated from the models for the NSAID and β-blocker outcomes were smaller than those used in the sample size calculations, whereas those for the ACE inhibitor or diuretic outcome were larger.
Table 3Prevalence of prescription and monitoring problems at 6 months' follow-up by allocation group
Adjusted odds ratio (95% CI)
Adjusted for randomisation stratum, baseline prevalence of errors, deprivation, and training status unless otherwise stated.
1 History of peptic ulcer prescribed an NSAID without a PPI/history of peptic ulcer without a PPI 86/2014 (4%) 51/1852 (3%) 0·58 (0·38–0·89) 4·68×10−7
2 Asthma prescribed a β blocker/asthma 658/22 224 (3%) 499/20 312 (2%) 0·73 (0·58–0·91) 3·50×10−7
3 Aged ≥75 years receiving long-term ACE inhibitors or loop diuretics without urea and electrolyte monitoring in the previous 15 months/aged ≥75 years receiving long-term ACE inhibitors or diuretics 436/5329 (8%) 255/4851 (5%) 0·51 (0·34–0·78) 0·14
2a Asthma and not CHD prescribed a β blocker/asthma and not CHD 387/21 048 (2%) 299/19 286 (2%) 0·81 (0·63–1·04) 4·94×10−6
4 History of venous or arterial thrombosis prescribed combined oral contraceptives/history of venous or arterial thrombosis (women) 8/2783 (<1%) 3/2490 (<1%) 0·39 (0·07–2·15) 0·05
5a Methotrexate for ≥3 months without full blood count in past 3 months/methotrexate for ≥3 months 162/518 (31%) 122/494 (25%) 0·80 (0·45–1·43) 0·15
5b Methotrexate for ≥3 months without a liver function test in past 3 months/methotrexate for ≥3 months 154/518 (30%) 121/494 (24%) 0·79 (0·43–1·45) 0·17
6 Warfarin for ≥3 months without an INR in past 3 months/warfarin for ≥3 months 78/1618 (5%) 52/1720 (3%) 0·53 (0·29–0·95) 1·11×10−6
7 Lithium for ≥3 months without a lithium concentration measurement in past 3 months/lithium for ≥3 months 84/211 (40%) 67/190 (35%) 0·53 (0·24–1·19) 0·24
8 Amiodarone for ≥6 months without a thyroid function test in the past 6 months/amiodarone for ≥6 months 106/235 (45%) 81/242 (33%) 0·57 (0·36–0·92) 4·86×10−7
9 Methotrexate without instructions to take weekly/patients prescribed methotrexate 16/310 (5%) 2/268 (1%) 0·72 (0·06–9·25) 5·20×10−7
10 Amiodarone for ≥1 month at a dose of >200 mg per day/amiodarone for ≥1 month 1/228 (<1%) 1/228 (<1) 0·96 (0·06–15·55)
Adjustment for other variables not calculable.
2·1×105
11 At least one prescription problem/at risk of at least one prescription problem 752/26 329 (3%) 553/24 073 (2%) 0·71 (0·59–0·86) 9·16×10−7
12 At least one monitoring problem/at risk of at least one monitoring problem 868/7409 (12%) 584/6963 (8%) 0·56 (0·44–0·70) 0·04
Data are numerator/denominator (%), unless otherwise stated. Numbers of patients does not equal the sum of the denominators in each group because only those with baseline and follow-up data are included. PINCER=pharmacist-led information technology intervention. NSAID=non-steroidal anti-inflammatory drug. ACE=angiotensin converting enzyme. PPI=proton-pump inhibitor. CHD=coronary heart disease. INR=international normalised ratio. ICC=intraclass correlation coefficients.
* Adjusted for randomisation stratum, baseline prevalence of errors, deprivation, and training status unless otherwise stated.
† Adjustment for other variables not calculable.
We identified issues with three of our secondary outcome measures.
As a result, we excluded seven practices from the analysis of outcome 6, and 11 practices from outcomes 9 and 10. Participants in the PINCER group were significantly less likely to have a prescription error or monitoring problem (table 3). Participants were also significantly less likely to have been prescribed warfarin without monitoring in the previous 3 months or prescribed amiodarone without a thyroid function test in the past 6 months (table 3). Treatment groups did not differ for other secondary outcome measures. We identified no significant interactions between treatment group and practice size or deprivation (data not shown).
At 12-months' follow-up participants in the PINCER group were still significantly less likely to have been prescribed a β blocker if they had a history of asthma, or prescribed an ACE inhibitor or diuretic without assessment of urea and electrolytes in the past 15 months (table 4). However, prescription of a non-selective NSAID without a PPI for patients with a history of peptic ulcer was no longer significant (table 4). The intraclass correlation coefficients estimated from the models for the NSAID outcome were smaller than those used in the sample size calculations, and those for the β blocker and ACE inhibitors or diuretic outcomes were larger.
Table 4Prevalence of prescription and monitoring problems at 12 months' follow-up by allocation group
Adjusted odds ratio
(95% CI)
2 Asthma prescribed a β blocker/asthma 692/23 520 (3%) 545/21 359 (3%) 0·78 (0·63–0·97) 0·008
Secondary outcome measures
2a Asthma and not CHD prescribed a β blocker/asthma and not CHD 414/22 294 (2%) 326/20 283 (2%) 0·79 (0·62–1·02) 0·009
4 History of venous or arterial thrombosis prescribed combined oral contraceptives/history of venous or arterial thrombosis (women) 15/2987 (1%) 4/2640 (<1%) 0·57 (0·05–6·17) 0·24
5b Methotrexate for ≥3 months without a liver function test in past 3 months/methotrexate for ≥3 months 186/552 (34%) 134/531 (25%) 0·50 (0·28–0·91)
Includes interaction between treatment group and covariate dichotomised at the median value (≤median vs >median).
0·16
6 Warfarin for ≥3 months without an INR in past 3 months/warfarin for ≥3 months 69/1752 (4%) 76/1877 (4%) 0·98 (0·52–1·85) 0·10
8 Amiodarone for ≥6 months without a thyroid function test in the past 6 months/amiodarone for ≥6 months 92/247 (37%) 80/233 (34%) 0·77 (0·41–1·43) 0·11
9 Methotrexate without instructions to take weekly/patients prescribed methotrexate 13/309 (4%) 0/271 (0%) Not calculable ..
10 Amiodarone for ≥1 month at a dose of >200 mg per day/amiodarone for ≥1 month 1/231 (<1%) 1/232 (<1%) 0·95 (0·06–15·45)
1·07×10−5
11 At least one prescription problem/at risk of at least one prescription problem 785/27 808 (3%) 610/25 246 (2%) 0·78 (0·64–0·94) 0·01
12 At least one monitoring problem/at risk of at least one monitoring problem 901/8011 (11%) 652/7449 (9%) 0·64 (0·51–0·82)
Data are numerator/denominator (%), unless otherwise stated. Number of patients does not equal the sum of the denominators in each group, because only those with baseline and follow-up data are included. PINCER=pharmacist-led information technology intervention. NSAID=non-steroidal anti-inflammatory drug. ACE=angiotensin converting enzyme. PPI=proton-pump inhibitor. CHD=coronary heart disease. INR=international normalised ratio. ICC=intraclass correlation coefficients.
† Includes interaction between treatment group and covariate dichotomised at the median value (≤median vs >median).
‡ Adjustment for other variables not calculable.
Participants in the PINCER group were overall still significantly less likely to have a prescription or monitoring problem at 12 months (table 4), and were significantly less likely to have been prescribed methotrexate without a full blood count or liver function test in the past 3 months (table 4). Patients were also significantly less likely to have been prescribed lithium without measurement of lithium concentration in the past 3 months (table 4). Treatment groups did not differ for other secondary outcome measures (table 4). We identified no significant interactions between treatment group and practice or practice deprivation (data not shown). The models for primary outcomes, and all except three models for secondary outcomes, were robust to the exclusion of practices with standardised empirical Bayes estimates of random effects above or below two standard deviations (data not shown). No adverse events were reported.
Three reports were run in each practice (at baseline, 6 months, and 12 months), at a cost of £92·84 (£1=US$1·56) per practice at 6 months follow-up, and £139·26 per practice at 12 months follow-up. In total, generation of reports cost £3342·24 for 36 simple feedback and 36 PINCER intervention practices at 6 months and £5013·36 for the same number of practices at 12 months. This was the only cost in the simple feedback group. The PINCER group also generated training costs of £9933·26, preparation costs of £102·89, facilitated meeting costs of £6976·81, monthly meeting costs of £1996·30, practice meeting costs of £794·52, and error management costs of £14 641·20. The cost components were added to give the total mean cost per practice in each group of the trial (webappendix).
Adjusted costs were estimated by generalised linear modelling with the assumption of a γ distribution (webappendix). Only baseline list size was significant (webappendix).
Table 5 summarises the costs and outcomes used in the incremental cost-effectiveness analysis and figure 2 shows the cost-effectiveness acceptability curves at 6 months and 12 months. This analysis suggests that PINCER had a 95% probability of being cost effective if the decision-maker's ceiling willingness to pay reached £75 per error avoided (at 6 months) or £85 per error avoided (at 12 months). Because the error reduction is sustained at 12 months, this analysis suggests that the intervention could be delivered yearly, rather than every 6 months, and still retain equivalent cost-effectiveness.
Table 5Costs, outcomes, and incremental economic analyses associated with PINCER intervention and simple feedback
PINCER intervention
Group comparison
Mean cost per practice (median, range)
Report generation
6 months £92·84 (NA) £92·84 (NA) ..
12 months £139·26 (NA) £139·26 (NA) ..
Pharmacist training £0 £275·92 (267·76, 79·54 to 591·23) ..
Quarterly facilitated strategic meetings £0 £195·23 (189·45, 56·28 to 418·33) ..
Monthly operational meetings £0 £56·88 (55·20, 16·40 to 121·88) ..
Practice feedback £0 £22·07 (21·42, 6·36 to 47·29) ..
Management of errors £0 £406·70 (320·93, 57·04 to 1318·68) ..
6 months £92·84 (NA) £1049·67 (967·86, 329·22 to 2086·78) ..
12 months £139·26 (NA) £1096·09 (1014·28, 375·64 to 2133·20) ..
Mean incremental cost (SD, 95% CI)
6 months .. .. £871·88 (54·04, 765·96 to 977·79)
12 months .. .. £870·63 (53·60, 858·42 to 1 068·52)
Mean incremental errors (SD, 95% CI)
6 months .. .. −12·90 (0·26, −13·42 to −12·39)
12 months .. .. −12·71 (0·29, −13·27 to −12·14)
Mean ICER (2·5th–97·5th percentile; cost per error avoided)
6 months .. .. £65·60 (58·2 to 73·0)
12 months .. .. £66·53 (66·8 to 81·5)
£1=US$1·56. NA=not applicable. ICER=incremental cost-effectiveness ratio. PINCER=pharmacist-led information technology-based intervention.
Figure 2Cost-effectiveness acceptability curves at 6 and 12 months
PINCER=pharmacist-led information technology-based intervention.
This trial shows that the PINCER intervention is more effective than simple feedback for reduction of the numbers of patients at risk from hazardous prescribing and inadequate blood-test monitoring of medicines in general practice. In view of the high risk of serious iatrogenic harm associated with errors, reductions of the magnitude noted in this trial are likely to be clinically important.
The main strengths of this study include the pragmatic design, the large numbers of practices enrolled, and the range of clinically relevant outcomes tested. We avoided bias in allocation of practices by use of an independent centralised web-based randomisation service, and the sequence of treatment allocations was masked until all data analyses had been completed. Also, none of the practices were lost to follow-up. Because the data were extracted electronically from general practice computer systems, and those who cleaned the data were masked to treatment allocation, no risk of bias existed. Analyses were undertaken independently by two statisticians, masked to treatment allocation, both producing similar findings. When minor differences were identified, the reasons for these were explored and discrepancies resolved. Although some of the intraclass correlation coefficients estimated from our models were larger than those used in our sample size calculations, this difference did not result in insufficient power because significant effects were shown for those outcomes.
The simple feedback we used as a control is superior to routine models of care used in the UK, therefore the true effect size of PINCER in comparison to standard care might have been underestimated. We have also sought to assess the sustained effect of PINCER at 12 months after the intervention.
We have undertaken an embedded longitudinal qualitative assessment, which shows that general practice staff are receptive to the PINCER intervention and that the introduction of the intervention was a valuable educational experience for them (Creswell K, unpublished). Our parallel cohort study in 438 practices throughout England confirms that the trial's findings can be generalised widely (Shiekh A, unpublished).
However, our trial does have some limitations. Practices that agreed to participate and those that did not differed slightly (webappendix). The characteristics of the groups were much the same, but the intervention practices were slightly more deprived and likely to be training practices than were control practices (webappendix). We therefore adjusted for these two factors in our analysis. Moreover, results of our parallel national cohort study showed that the frequency of these errors was much the same in a large sample of non-intervention practices, suggesting that the practices enrolled in this trial are likely to be representative of practices nationwide. Although data extraction for our prespecified primary outcome measures worked successfully in all general practices recruited for the study, we encountered some difficulties with three of the secondary outcome measures. Finally, our sample size calculations were made on the basis of assessment of outcomes at the main 6 month assessment point; therefore the study might have been underpowered to assess outcomes at 12 months.
We deliberately focused on potential medication errors rather than adverse events and therefore we cannot be certain that the pharmacist-led intervention will reduce harm to patients. Nevertheless, a strong argument can be made for focus on the measurement of errors rather than on adverse events for assessment of the quality of clinical practice, as the former relate most closely to actions that are within the control of health-care professionals.
Lilford RJ
Brown CA
Nicholl J
Use of process measures to monitor the quality of clinical practice.
We are developing an economic model incorporating the clinical and economic effects of changes in error rates to allow calculation of costs per quality-adjusted life-years. Little equivalent work about this topic has been done. An exploratory economic modelling study
Karnon J
Czoski-Murray C
Model-based cost-effectiveness analysis of interventions aimed at preventing medication error at hospital admission (medicines reconciliation).
J Eval Clin Pract. 2009; 15: 299-306
estimated that a pharmacy-based intervention to reduce medication errors would have a probability of being cost effective with a quality-adjusted life-year value of £10 000 of more than 60%. This finding suggests that pharmacist-led interventions to reduce error rates have the potential to be cost effective according to currently used cost-effectiveness thresholds. The webappendix contains further discussion of the economic analysis.
Since the start of our trial, important studies have questioned the effectiveness of isolated pharmacist-centred interventions in general practice. For example, the HOMER trial
aimed to assess whether home-based medication review by pharmacists in older people would affect hospital readmission rates. The researchers reported an increase in hospital admissions and no improvement in quality of life or death rate. The RESPECT trial
showed no benefit of involvement of community pharmacists in the moderation of drug management (pharmaceutical care) in older people in general practice. Findings are awaited from a trial in the USA assessing the effectiveness of a pharmacist intervention for reduction of medication errors after hospital discharge for high-risk patients with cardiovascular disease.
Roumie CL
Cawthon C
Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL–CVD) study.
Circ Cardiovasc Qual Outcomes. 2010; 3: 212-219
In view of the conflicting evidence for the effectiveness of pharmacist-led interventions in primary care, the effectiveness of PINCER should be considered (panel 2). First, we used an educational outreach approach, which is a moderately powerful intervention for changing professional behaviour.
Pharmacists who took part in the trial received training on the use of these techniques and the evidence base for the outcome measures used in the trial. Second, we focused on specific examples of hazardous prescription or inadequacies in medication monitoring, which might have increased our ability to detect change compared with more generalised measures. Third, the pharmacists established working relations with the practices, which granted them access to patient records for contextual information and the mandate to provide practical support to make changes to patients' medications and organise blood tests. Fourth, the intervention was multifaceted, used the potential offered by information technology, and aimed to simultaneously tackle different barriers to change; such interventions are known to be more effective than are simpler interventions.
Grimshaw JM
Shirran L
Changing provider behavior: an overview of systematic reviews of interventions.
Med Care. 2001; 39: II2-I45
Research in context
Systematic reviews have shown the high incidence of prescription errors in primary care, identified the errors that are most likely to result in patient harm, and identified the processes in which such errors most frequently occur, which suggest potential interventions to reduce the frequency of these errors.
Winterstein AG
Sauer BC
Hepler CD
Poole C
The incidence of preventable drug-related hospital admissions.
Pharmacoepidemiol Drug Saf. 2000; 9: S118-S147
Garfield S
Barber N
Walley P
Willson A
Eliasson L
Quality of medication use in primary care—mapping the problem, working to a solution: a systematic review of the literature.
BMC Med. 2009; 7: 50
Black AD
Car J
Pagliari C
The impact of eHealth on the quality and safety of health care: a systematic overview.
We searched the Cochrane Library, Medline, and Embase with the term "systematic review AND prescribing errors AND primary care" for reports published between Jan 1, 1980, and Aug 31, 2011, in any language. Our searches identified only one systematic review,
which focuses on primary care, assessing the effectiveness and cost-effectiveness of interventions aiming to reduce the frequency of prescription errors in primary care settings.
This trial shows that a pharmacist-delivered information technology intervention substantially reduced the frequency of a range of clinically important prescription and medication monitoring errors. Related qualitative work shows the acceptability of the PINCER intervention to general practices and a parallel longitudinal observational study of prescription errors in over 400 practices shows the high probable generalisability of these findings across the UK.
Because of the pressing need to reduce errors in health care,
PINCER offers an effective method for reducing a range of medication errors in general practice. An essential prerequisite is the use of electronic health records, which effectively reduces errors.
The intervention that we have developed will be suitable for implementation in the increasing number of countries where clinical records are now computerised and where the roles of pharmacists to monitor proactively for clinically important medication errors can be extended.
This online publication has been corrected. The corrected version first appeared at thelancet.com on June 15, 2012
AJA helped design the study, was partly responsible for the overall administration and direction of the study, the analysis and interpretation of data. AS and JAC were partly responsible for the conception, overall design, and administration of the study. SR was the trial manager, helped design the Quest Browser queries, and piloted the data extraction methods. SA, RAE, RH, DK, CJM, and RJP helped design the project. AS, SA, DK, and RJP designed the statistical analyses. SA and DK did the statistical analyses. RE did the economic analysis with assistance from KP, MB, and MF, who contributed to the design, data collection, data processing, data analysis, and interpretation of the findings. GS advised on the study and commented on trial documentation. KC and ME collected data and interpreted the findings; ME processed and cleaned data. AJA, SA, JAC, KC, ME, RAE, RH, DK, RJP, SR, AS, and GS helped design and do the trial, analysed data, and interpreted the findings. AJA, SR, and AS wrote the first draft. All authors read and approved the final manuscript. AJA had full access to all the data in the study and had final responsibility for the decision to submit for publication.
The study was funded by a research grant awarded by the Department of Health's Patient Safety Research Portfolio. We thank the PINCER trial pharmacists, primary care trusts, general practices, and patients who took part in the study; Rachel Illingworth for facilitating the employment and management of the trial pharmacists; Sharon Mills for administrative support and assistance with collation of the costs for the pharmacists' interventions; Clive Morris and colleagues from The Computer Room (www.tcrnottingham.com) and Ed Longridge for their input about data extraction from general practices; Casey Quinn for contributions to the analysis of incremental cost-effectiveness ratios; Tom Turner for provision of advice about written materials that were issued to patients; Richard Lilford and colleagues from the Patient Safety Research Portfolio for their support. Finally, we thank the members of the independent Trial Steering Committee, Philip Hannaford (chair), Martin Buxton, and Marjorie Weiss; and the Data Monitoring and Ethics Committee, Richard Baker (chair), Christine Bond, and Peter Donnan for overseeing the trial.
Web Extra Material
Supplementary webappendix
(accessed Jan 20, 2012).
© 2012 Elsevier Ltd. Published by Elsevier Ltd.
Improving medication safety
Adverse drug events1 are a major problem for patient safety. A systematic review estimated that 18 adverse drug events per 100 outpatients, including almost seven preventable events, occur yearly.2 Preventable adverse drug events are caused by errors such as inappropriate prescription, medication discrepancies, non-adherence, and failure to monitor for side-effects or disease control. More than 20 years of research into medication safety has shown the efficacy of several interventions, including computer provider order entry, electronic decision support, and some pharmacist interventions.
Department of Error
Avery AJ, Rodgers S, Cantrill JA, et al. A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 2012; 379: 1310–19—In the Summary of this Article (April 7), Methods, line 4, the details of masking should have been: "The allocation was masked to researchers and statisticians involved in processing and analysing the data. The allocation was not masked to general practices, pharmacists, patients, or researchers who visited practices to extract data." This correction has been made to the online version as of June 15, 2012.


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text:
Q: How to arrange points in a particular ggplot? I am trying to plot the estimates of discrimination parameters from a Bayesian factor analysis model. Here is the code I have written:
plotting <- read.csv("mcmc_test_summary.csv")

plot  <-
  ggplot(data = plotting,
         aes(x = variable, y = Mean)) +
  geom_point()  +
  geom_errorbar(aes(ymin = (Mean - 1.96 * SD),
                    ymax = (Mean + 1.96 * SD)), width = 0) +
  xlab("Variable") +
  ylab("Discrimination Parameter") +
  coord_flip() +
  geom_hline(yintercept = 0, linetype = 2) +
  theme_bw()
plot

This yields the following plot:

I want the points to be arranged such that the lowest values start from the bottom and the highest is on top, thereby giving the plot a funnel-like shape. What is the best way to accomplish this?
Here are some sample data:
structure(list(Mean = c(0.220902135, 0.92194934, 0.951757326,
0.840550594, -0.150822916, -0.329221189, 0.328728933, 0.339120204,
0.175737064, -0.023259214, 0.518680416, 0.424380437, 0.069486045,
0.387331157, -0.046630424, -0.479194489, -0.031897813, -0.032256682,
-0.273993931, 0.001829565, 0.636323045, 0.075309094, 0.341434738
), SD = c(0.054641356, 0.044146774, 0.042821487, 0.046534239,
0.057593389, 0.05600894, 0.054866676, 0.054378415, 0.056661627,
0.056885927, 0.053209264, 0.053752882, 0.055792144, 0.054248883,
0.055669106, 0.053439085, 0.056338517, 0.056278977, 0.055108844,
0.05538902, 0.049911011, 0.045537741, 0.054748323), Naive.SE = c(0.000546414,
0.000441468, 0.000428215, 0.000465342, 0.000575934, 0.000560089,
0.000548667, 0.000543784, 0.000566616, 0.000568859, 0.000532093,
0.000537529, 0.000557921, 0.000542489, 0.000556691, 0.000534391,
0.000563385, 0.00056279, 0.000551088, 0.00055389, 0.00049911,
0.000455377, 0.000547483), Time.series.SE = c(0.000537287, 0.000441468,
0.000428215, 0.000465342, 0.000610109, 0.000560089, 0.000548667,
0.000529486, 0.000557413, 0.000568859, 0.000538941, 0.000537529,
0.000546465, 0.000528167, 0.000556691, 0.000534391, 0.000563385,
0.00056279, 0.000551088, 0.000544834, 0.00049911, 0.000455377,
0.000547483), variable = c("v2exremhsp_ord", "v2exdfdshs_ord",
"v2exdfvths_ord", "v2exdfpphs_ord", "v2lgqstexp_ord", "v2lginvstp_ord",
"v2lgoppart_ord", "v2lgfunds_ord", "v2lgcrrpt_ord", "v2lglegpup_ord",
"v2lglegplo_ord", "v2exaphogp", "v2lgcomslo_ord", "v2lgsrvlo_ord",
"v2lgstafflo_ord", "v2lgintbup", "v2exaphos", "v2ex_legconhos",
"v2lgintblo", "v2lgtreaty", "v2lgwarlaw", "v2lgelecup", "v2lgello"
), parameter = c("discrimination", "discrimination", "discrimination",
"discrimination", "discrimination", "discrimination", "discrimination",
"discrimination", "discrimination", "discrimination", "discrimination",
"discrimination", "discrimination", "discrimination", "discrimination",
"discrimination", "discrimination", "discrimination", "discrimination",
"discrimination", "discrimination", "discrimination", "discrimination"
), cilow = c(0.219831164, 0.921084063, 0.950918024, 0.839638523,
-0.151951746, -0.330318965, 0.327653546, 0.338054387, 0.174626496,
-0.024374179, 0.517637515, 0.423326881, 0.068392519, 0.386267879,
-0.047721538, -0.480241895, -0.033002048, -0.03335975, -0.275074064,
0.000743941, 0.635344789, 0.074416554, 0.340361671), cihigh = c(0.221973105,
0.922814617, 0.952596627, 0.841462665, -0.149694086, -0.328123414,
0.32980432, 0.340186021, 0.176847632, -0.02214425, 0.519723318,
0.425433994, 0.070579571, 0.388394435, -0.045539309, -0.478147083,
-0.030793578, -0.031153614, -0.272913798, 0.00291519, 0.637301301,
0.076201633, 0.342507805)), class = "data.frame", row.names = c(NA,
-23L))
        Mean         SD
1  0.2209021 0.05464136
2  0.9219493 0.04414677
3  0.9517573 0.04282149
4  0.8405506 0.04653424
5 -0.1508229 0.05759339
6 -0.3292212 0.05600894
     Naive.SE Time.series.SE
1 0.000546414    0.000537287
2 0.000441468    0.000441468
3 0.000428215    0.000428215
4 0.000465342    0.000465342
5 0.000575934    0.000610109
6 0.000560089    0.000560089
        variable      parameter
1 v2exremhsp_ord discrimination
2 v2exdfdshs_ord discrimination
3 v2exdfvths_ord discrimination
4 v2exdfpphs_ord discrimination
5 v2lgqstexp_ord discrimination
6 v2lginvstp_ord discrimination
       cilow     cihigh
1  0.2198312  0.2219731
2  0.9210841  0.9228146
3  0.9509180  0.9525966
4  0.8396385  0.8414627
5 -0.1519517 -0.1496941
6 -0.3303190 -0.3281234

A: You could reorder your variable based on your Mean value like this:
library(ggplot2)
plot  <-
  ggplot(data = plotting,
         aes(x = reorder(variable, Mean), y = Mean)) +
  geom_point()  +
  geom_errorbar(aes(ymin = (Mean - 1.96 * SD),
                    ymax = (Mean + 1.96 * SD)), width = 0) +
  xlab("Variable") +
  ylab("Discrimination Parameter") +
  coord_flip() +
  geom_hline(yintercept = 0, linetype = 2) +
  theme_bw()
plot

Created on 2023-01-20 with reprex v2.0.2

Using -Mean in reorder will do the opposite:
library(ggplot2)
plot  <-
  ggplot(data = plotting,
         aes(x = reorder(variable, -Mean), y = Mean)) +
  geom_point()  +
  geom_errorbar(aes(ymin = (Mean - 1.96 * SD),
                    ymax = (Mean + 1.96 * SD)), width = 0) +
  xlab("Variable") +
  ylab("Discrimination Parameter") +
  coord_flip() +
  geom_hline(yintercept = 0, linetype = 2) +
  theme_bw()
plot

Created on 2023-01-20 with reprex v2.0.2

redpajama_set_name: RedPajamaStackExchange