author: Ferid Murad
date: 2012
institute: 1George Washington University, Washington, D.C., USA
title: Discovery of nitric oxide and cyclic GMP in cell signaling and their role in drug development

The role of nitric oxide in cellular signaling in the past three decades has become one of the most rapidly growing areas in biology. Nitric oxide is a gas and a free radical with an unshared electron that can regulate an ever-growing list of biological processes. Nitric oxide is formed from L-arginine by a family of enzymes called nitric oxide synthases. These enzymes have a complex requirement for a number of cofactors and regulators including NADPH, tetrahydrobioterin, flavins, calmodulin and heme. The enzymes are present in most cells and tissues. In many instances, nitric oxide mediates its biological effects by activating the soluble isoform of guanylyl cyclase and increasing cyclic GMP synthesis from GTP. Cyclic GMP, in turn, can activate cyclic GMP-dependent protein kinase (PKG) and can cause smooth muscles and blood vessels to relax, decrease platelet aggregation, alter neuron function, etc. These effects can decrease blood pressure, increase blood flow to tissues, alter memory and behavior, decrease blood clotting, etc. The list of effects of nitric oxide that are independent of cyclic GMP formation is also growing at a rapid rate. For example, nitric oxide can interact with transition metals such as iron, thiol groups, other free radicals, oxygen, superoxide anion, unsaturated fatty acids, and other molecules. Some of these reactions result in the oxidation of nitric oxide to nitrite and nitrate to terminate the effect, while other reactions can lead to altered protein structure function and/or catalytic capacity. These effects probably regulate bacterial infections, inflammation of tissues, tumor growth, and other disorders. These diverse effects of nitric oxide that are cyclic GMP dependent or independent can alter and regulate numerous important physiological events in cell regulation and function. Nitric oxide can function as an intracellular messenger, an antacoid, a paracrine substance, a neurotransmitter, or as a hormone that can be carried to distant sites for effects. Thus, it is a unique molecule with an array of signaling functions. However, with any messenger molecule, there can be too little or too much of the substance, resulting in pathological events. Some of the methods to regulate either nitric oxide formation metabolism, or function have been in clinical use for more than a century, as with the use of organic nitrates and nitroglycerin in angina pectoris that was initiated in the 1870s. Inhalation of low concentrations of nitric oxide can be beneficial in premature infants with pulmonary hypertension and increase survival rates. Ongoing clinical trials with nitric oxide synthase inhibitors and nitric oxide scavengers are examining the effects of these agents in septic shock, hypotension with dialysis, inflammatory disorders, cancer therapy, etc. Recognition of additional molecular targets in the areas of nitric oxide and cyclic GMP research will continue to promote drug discovery and development programs in the field. Current and future research will undoubtedly expand the clinician's therapeutic armamentarium to manage a number of important diseases by perturbing nitric oxide formation and metabolism. Such promise and expectations have obviously fueled the interests in nitric oxide research for a growing list of potential therapeutic applications. There have been and will continue to be many opportunities from nitric oxide and cyclic GMP march to develop novel and important therapeutic agents. There are presently more than 80,000 publications in the area of nitric oxide research. The lecture will discuss our discovery of the first biological effects of nitric oxide and how the field has evolved since our original reports in 1977. The possible utility of this signaling pathway to facilitate novel drug development and the creation of numerous projects in the Pharmaceutical and biotechnology industrials will also be discussed.


abstract: A report on work presented at the 39th Annual Meeting of the American Society for Cell Bilogy, Washington DC, December 11-15, 1999
  .
  Conference website: <http://www.faseb.org/ascb/meetings/am99/main99mtg.htm>
author: Janet Rossant
date: 2000
institute: 1Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X5, Canada
title: How will knowing genome sequences affect the life of the average cell biologist?

The impact of 'the genome project' on all aspects of biomedical research in the next century cannot be underestimated. We will know the complete sequence of the human genome and of the genomes of an ever-increasing number of model organisms, providing masses of new raw genetic data to sustain the study of any particular cellular process or pathway. But perhaps more importantly, the tools and mind-set of the genomicist seem destined to alter the way we all think about and practise our science. Once we know the complete set of genes that exist in an organism, it will no longer be acceptable to focus on one gene at a time, to study its effects in terms of a read-out consisting of a few other related genes and cellular events, and then to conclude that we have understood its function. Conversely, if we want to understand a cellular process, we will need somehow to integrate all the molecular interactions that occur, rather than being satisfied with cataloguing the few major pathway components identified by genetic or biochemical screens. Genomics, and its newer siblings functional genomics, proteomics and bioinformatics, are teaching us all to think in a high-throughput, genome-wide manner and are providing the technologies to allow us to translate these thoughts into action.

The pace of technological progress in this area can be intimidating, to the established investigator and the new graduate student alike. No wonder, then, that there was a packed house at the opening symposium - entitled 'The Impact of Genome-wide Studies on Cell and Developmental Biology' - of the 39th American Society for Cell Biology meeting. David Botstein (Stanford University Medical School) began by emphasizing the importance of model organisms and comparative genomics in assigning meaning to the sequence of As, Ts, Cs and Gs that will make up the final product of the Human Genome Project. The complete sequence of the genome of the yeast *Saccharomyces cerevisiae* has been available since 1996, and that of the worm *Caenorhabditis elegans* since 1998. As detailed by Gerry Rubin (University of California Berkeley), 'version one' of the 185 Mb *Drosophila* genome is also complete, as a result of a collaboration between academic centers in the United States and Europe and the company Celera Genomics. A draft sequence of the human genome is expected to follow shortly, with the complete sequence due to be finished by 2003 at the latest.

With the sequence information in the databases to date, it is already possible to query the genome in interesting ways. Botstein pointed out that comparison of yeast and worm genomes has identified a set of conserved genes encoding core biological functions, such as metabolism, DNA replication and protein trafficking. These genes make up 40% of the yeast and 20% of the worm genome. Alongside this core set of proteins - which are expected to be common to single-cell and multicellular eukaryotes - worms, flies and humans have developed a complex set of genes involved in signal transduction and gene regulation, in order to accomplish their elaborate developmental programs. These pathways show less absolute conservation of gene sequence, but general classes of proteins and protein domains can be recognized across evolution.

The extent of cross-species conservation across the whole range of gene function is, in fact, remarkable. Of the human genes known to date, 74% have related sequences in the worm. Cori Bargmann (University of California, San Francisco) illustrated how inter- and intra-species sequence comparisons of gene families can confirm conserved functions and identify new ones, by scanning the *C. elegans* genome for genes encoding ion channels and G-protein-coupled receptors. There is no voltage-activated sodium channel identifiable in the *C. elegans* genome, consistent with the results of electrophysiological studies, but voltage-regulated calcium and potassium channels are found, some similar to vertebrate counterparts and some divergent. Clearly, some aspects of cell excitability are common and fundamental and some are subject to evolutionary pressure related to the adaptation of the organism to its particular environment. Despite the extensive prior mutational analysis of the worm, the genome sequence has thrown up many genes that were not identified by mutation. For example, Bargmann's lab cloned one odorant receptor gene, *Od-10*, through mutational analysis. It encodes a novel G-protein-coupled receptor. Analysis of the genome sequence revealed about 1,000 'orphan' G-protein-coupled receptors, many of which appear to be related to Od-10 and which probably represent more odorant receptors. Overnight, then, a lab working on one gene finds itself working on hundreds.

The need for systematic, genome-wide methods for assigning gene function to this deluge of sequence information was a common theme of the symposium. Annotation by sequence similarity is the usual first step, but it is difficult and can be misleading. Genes of similar sequence may have acquired new functions during evolution. For example, mutational analysis of a *Drosophila* gene whose sequence suggested that it could be a chitinase enzyme revealed instead that it was a novel growth factor (Rubin). Developing a common language for gene annotation across different databases is one of the steps needed to improve the situation. This is the goal of the Gene Ontology (GO) project, a collaborative effort between workers on the yeast, fly and mouse genome databases. But even a common language and more sophisticated sequence analysis tools are not likely yet to replace the need for the seasoned eye of the expert biologist when interpreting genome sequence. The publication of the *Drosophila* genome sequence depends on the completion of a major effort to annotate all the genes identified in the sequence to date, by a group of biologists expert in different gene families, working alongside bioinformatics experts.

Much more information about gene function can, of course, be gleaned from knowing expression patterns and the effects of loss or gain of function. Genome-wide initiatives in assessing expression and function are underway for all model organisms. For example, an assay of the function of all *C. elegans* genes, using RNA interference, for their effects on early cell division processes is under way at the European Molecular Biology Laboratory (poster presented by Pierre Gönczy). The Berkeley *Drosophila* Genome Project is surveying the expression of all *Drosophila* genes by whole-mount *in situ* hybridization in embryos and attempting a catalog of gene mutations by accumulating insertions of P elements or Gal4 activation domains into as many different sites in the genome as possible (Rubin). Major efforts like this in a few centers can provide enormous resources for the average cell or developmental biologist who wishes to pursue more detailed analysis of an individual gene or process of interest. Rubin noted that there are an estimated 15,000 *Drosophila* genes and 5,000 active *Drosophila* researchers, or three genes per biologist for future study. The general philosophy of the various academic genome initiatives - to share all information freely and rapidly via the internet - must surely apply to these next phases of large-scale functional genomics as well. A familiarity with database resources and the tools for database mining will therefore be essential in allowing biologists to gain the most from all the material made available by large-scale projects.

There are growing numbers of genome research applications, however, which the average biology lab will want to use directly for its own studies. None has generated more excitement than DNA microarrays or 'chips'. The cDNAs from all of the 6,000 or so yeast genes, a large proportion of the *Drosophila* and worm genes, and an increasing number of human and mouse genes, have been assembled onto glass microarrays in different labs world-wide. Hybridization analysis, combined with sophisticated clustering algorithms to analyse the results, allows analysis of gene expression changes in normal or abnormal states in unprecedented detail. Botstein has been a prime mover in validating this technology, along with his Stanford colleague, Pat Brown. Botstein described how yeast microarrays allow the identification of clusters of co-regulated genes whose expression changes together under a variety of different conditions. Because all the yeast genes are on a chip, complete sets of co-regulated genes can now be identified and studied. In larger organisms, microarrays are currently still sampling the genome, but a sample size of several thousand genes is sufficiently large to reveal unexpected patterns of co-regulated genes. Furthermore, microarrays are already finding utility in classifying tumor samples. 'Signature' patterns of gene expression can be discerned when many different tumor samples are compared, despite the potential heterogeneity imposed during sample collection (Botstein). These patterns can be used to define subclasses of tumors that are not apparent by more standard means. The possible applications of this technology to all aspects of biology seem bound to grow exponentially as the technology becomes more widely disseminated.

We are entering an exciting era, when genomes and genomics will provide the basis for a new form of truly integrative biological research. We cell and developmental biologists, who already think in integrative ways as a result of the questions our research addresses, need to be in the vanguard of this new biology.


date: 2017-04
title: WHO PUBLISHES LIST OF BACTERIA FOR WHICH NEW ANTIBIOTICS ARE URGENTLY NEEDED

**27 FEBRUARY 2017 \| GENEVA** - WHO today published its first ever list of antibiotic-resistant "priority pathogens" – a catalogue of 12 families of bacteria that pose the greatest threat to human health.

The list was drawn up in a bid to guide and promote research and development (R&D) of new antibiotics, as part of WHO's efforts to address growing global resistance to antimicrobial medicines.

The list highlights in particular the threat of gram-negative bacteria that are resistant to multiple antibiotics. These bacteria have built-in abilities to find new ways to resist treatment and can pass along genetic material that allows other bacteria to become drug-resistant as well.

"This list is a new tool to ensure R&D responds to urgent public health needs," says Dr Marie-Paule Kieny, WHO's Assistant Director-General for Health Systems and Innovation. "Antibiotic resistance is growing, and we are fast running out of treatment options. If we leave it to market forces alone, the new antibiotics we most urgently need are not going to be developed in time."

The WHO list is divided into three categories according to the urgency of need for new antibiotics: critical, high and medium priority.

The most critical group of all includes multidrug resistant bacteria that pose a particular threat in hospitals, nursing homes, and among patients whose care requires devices such as ventilators and blood catheters. They include Acinetobacter, Pseudomonas and various Enterobacteriaceae (including Klebsiella, E. coli, Serratia, and Proteus). They can cause severe and often deadly infections such as bloodstream infections and pneumonia.

These bacteria have become resistant to a large number of antibiotics, including carbapenems and third generation cephalosporins – the best available antibiotics for treating multi-drug resistant bacteria.

The second and third tiers in the list – the high and medium priority categories – contain other increasingly drug-resistant bacteria that cause more common diseases such as gonorrhoea and food poisoning caused by salmonella.

G20 health experts will meet this week in Berlin. Mr Hermann Gröhe, Federal Minister of Health, Germany says "We need effective antibiotics for our health systems. We have to take joint action today for a healthier tomorrow. Therefore, we will discuss and bring the attention of the G20 to the fight against antimicrobial resistance. WHO's first global priority pathogen list is an important new tool to secure and guide research and development related to new antibiotics."

The list is intended to spur governments to put in place policies that incentivize basic science and advanced R&D by both publicly funded agencies and the private sector investing in new antibiotic discovery. It will provide guidance to new R&D initiatives such as the WHO/Drugs for Neglected Diseases initiative (DNDi) Global Antibiotic R&D Partnership that is engaging in not-for-profit development of new antibiotics.

Tuberculosis – whose resistance to traditional treatment has been growing in recent years – was not included in the list because it is targeted by other, dedicated programmes. Other bacteria that were not included, such as streptococcus A and B and chlamydia, have low levels of resistance to existing treatments and do not currently pose a significant public health threat.

The list was developed in collaboration with the Division of Infectious Diseases at the University of Tübingen, Germany, using a multi-criteria decision analysis technique vetted by a group of international experts. The criteria for selecting pathogens on the list were: how deadly the infections they cause are; whether their treatment requires long hospital stays; how frequently they are resistant to existing antibiotics when people in communities catch them; how easily they spread between animals, from animals to humans, and from person to person; whether they can be prevented (e.g. through good hygiene and vaccination); how many treatment options remain; and whether new antibiotics to treat them are already in the R&D pipeline.

"New antibiotics targeting this priority list of pathogens will help to reduce deaths due to resistant infections around the world," says Prof Evelina Tacconelli, Head of the Division of Infectious Diseases at the University of Tübingen and a major contributor to the development of the list. "Waiting any longer will cause further public health problems and dramatically impact on patient care."

While more R&D is vital, alone, it cannot solve the problem. To address resistance, there must also be better prevention of infections and appropriate use of existing antibiotics in humans and animals, as well as rational use of any new antibiotics that are developed in future.

**WHO priority pathogens list for R&D of new antibiotics**

**Priority 1: CRITICAL**

1.  Acinetobacter baumannii, carbapenem-resistant

2.  Pseudomonas aeruginosa, carbapenem-resistant

3.  Enterobacteriaceae, carbapenem-resistant, ESBL-producing

**Priority 2: HIGH**

1.  Enterococcus faecium, vancomycin-resistant

2.  Staphylococcus aureus, methicillin-resistant, vancomycin-intermediate and resistant

3.  Helicobacter pylori, clarithromycin-resistant

4.  Campylobacter spp., fluoroquinolone-resistant

5.  Salmonellae, fluoroquinolone-resistant

6.  Neisseria gonorrhoeae, cephalosporin-resistant, fluoroquinolone-resistant

**Priority 3: MEDIUM**

1.  Streptococcus pneumoniae, penicillin-non-susceptible

2.  Haemophilus influenzae, ampicillin-resistant

3.  Shigella spp., fluoroquinolone-resistant

Available from: <http://www.who.int/mediacentre/news/releases/2017/bacteria-antibiotics-needed/en/>


abstract: With new technologies, concerns about gene patent claims regarding isolated DNA are becoming less relevant, but broad method claims could be more problematic.
author: Allison W Dobson; James P Evans
date: 2012
institute: 16628 Ridge Run Ct. Clemmons, NC 27012, USA; 2Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7264, USA
references:
title: Gene patents in the US - focusing on what really matters

As we move into the era of exome and whole-genome sequencing and witness a concurrent explosion in genomic data, concerns about related biotechnology patents must also shift accordingly. These are complex issues that are in a state of flux, and we find ourselves at an especially critical juncture given the US Supreme Court's recent treatment of several high-profile biotechnology cases. Here, we discuss recent developments in US patent law and the most pressing issues on the horizon, including pivotal issues surrounding the topic of patenting in genetics and biotechnology in the US. We particularly address some of the neglected or seemingly arcane points that nevertheless are critical to the researcher, medical practitioner, scientist, or indeed anyone seeking informed opinions about patent protection of new biotechnologies and genetic technologies. Striking the appropriate balance between intellectual property protection and public benefit is essential to promoting advances in the field.

The term 'gene patent' itself is ambiguous, and this term has been used loosely in the media to encompass a wide variety of patents related to genetics. Patents typically contain multiple claims in various forms, and each claim in a patent may stand or fall independently of the others. The subject matter claimed can be a tangible product (such as a DNA molecule) or a process indicated by gerunds (for example, 'constructing', 'connecting', or 'sequencing') that outline the steps of the method.

Both product and method claims are found in gene-related patents. Understanding the different claims encompassed by the imprecise term 'gene patent' is critical to the proper interpretation and application of patent law as we seek to optimally promote the development of genetic medicine. Because patent law is complicated (see Box 1 for a brief summary), the popular media understandably tends to avoid going into such differences in their attempts to simplify headlines about the topic \[1,2\]. The Supreme Court has recently cast serious doubt on broad method claims related to genes - but not on product claims.

A claim directed to 'isolated DNA' covers a single type of molecule, such as a PCR product, usually referring to a specific DNA sequence. It does not cover the sequence itself, as many professionals seem to believe. Thus, the patent holder is legally entitled to exclude others from making or using only that isolated DNA, based on that particular claim. Depending on how the claim is worded, the court may interpret it to cover isolated molecules with additional sequences that flank the named sequence (such as in a purified plasmid or other vector), but under no circumstances should a claim to isolated DNA preempt others from using its corresponding sequence as it exists within genomic DNA. That is the whole point of using the word 'isolated' - to clarify that the natural occurrence of that sequence is not what is claimed in the patent, but rather the physical molecule.

For scientists and physicians, the critical question should be whether any given patent stands in the way of their work. Isolated DNA claims are becoming less relevant to medical diagnostics, because sequencing a patient's DNA with modern (next-generation) technology is unlikely to infringe any product claim directed to an isolated DNA molecule. Thus, in a new genomic era, it is appropriate to refocus on broad method claims that have greater potential to affect the practice of medicine and medical research.

# The *Myriad* case: why it is important and where it stands today

As with many gene patents, the gene patent case *Association for Molecular Pathology et al. v. USTPO et al*. ('*Myriad*') \[3\] included both method and product claims, which are affected differently by recent court decisions. On 20 March 2012, a unanimous Supreme Court held in *Prometheus v. Mayo* ('*Prometheus*') \[4\] that Prometheus Laboratories' claims to methods of optimizing the dosage of drugs to treat gastrointestinal autoimmune diseases do not qualify as patentable subject matter under section 101 of the Patent Act (the section that specifically deals with what types of subject matter are eligible for patenting) \[5\].

*Prometheus* itself is not a gene patent case but has important implications for gene patents, especially claims of the method type. Thus, shortly thereafter, the Supreme Court issued a 'GVR order' for the *Myriad* case \[3\] in light of their decision in *Prometheus*. A GVR order means that in one swift motion, the Supreme Court (1) granted review of the *Myriad* case, (2) vacated the decision of the court of appeals below, and (3) remanded the case back to the court of appeals for further review in light of the new Supreme Court decision in *Prometheus*. The justices apparently believe that the court of appeals might find something relevant to the *Myriad* case within their analysis of *Prometheus*. Unfortunately, the patent practitioner community is having a difficult time figuring out what that might be, and therefore a large number of biotechnology patents currently stand in limbo \[6\]. One reporter wrote, 'It is apparently possible to split legal hairs endlessly over whether DNA is or is not patentable, and these patent claims will certainly be fought to the bitter end, as they are a crucial test case for future decisions' \[1\].

The Court of Appeals for the Federal Circuit (CAFC) is the US court that hears first appeals on all US patent cases. In March 2010, in the *Myriad* case, a Federal District Court judge invalidated all of Myriad Genetics' challenged gene patents. Myriad appealed to the CAFC, and in July 2011 the CAFC announced its decision. That decision partially reversed that of the District Court on many of Myriad's challenged 'isolated DNA' product claims, finding them to encompass legitimately patentable subject matter under section 101 of the Patent Act.

But the plaintiffs (the patent challengers) actually won some very important aspects of the case at the CAFC level the first time. Although the CAFC decision upheld many of the product claims at issue, it also affirmed the lower court's invalidation of Myriad's broad method claims. This part of the decision has received little fanfare. A closer look suggests that disproportionately heightened public concern over isolated DNA product claims, although understandable, is misplaced and that we should keep our eye on the right ball - in this case, the outcome of deliberations regarding the eligibility of the method claims.

A representative method claim from Myriad's US Patent No. 6,033,857 is:

Claim 1: A method for identifying a mutant *BRCA2* nucleotide sequence in a suspected mutant *BRCA2* allele which comprises **comparing** the nucleotide sequence of the suspected mutant *BRCA2* allele with the wild-type *BRCA2* nucleotide sequence, wherein a difference between the suspected mutant and the wild-type sequences identifies a mutant *BRCA2* nucleotide sequence. \[our emphasis\]

The CAFC decision rejected these method claims to comparing *BRCA2* sequences on the basis that they recite 'nothing more than abstract mental steps'. The Supreme Court has previously said that the only recognized exceptions for patent eligibility, at the most basic level (section 101), are 'laws of nature, physical phenomena, and abstract ideas' \[7\]. Further guidance for determining when claims cover abstract ideas is scant, but methods comprising purely mental steps such as comparing have consistently been found to encompass abstract ideas \[8\].

When the Supreme Court decided *Prometheus*, they said that the claims in dispute, which describe a process (or method) of adjusting drug dosage based on metabolite levels, essentially monopolized laws of nature. The relevant laws of nature, according to the court, are the 'relationships between concentrations of certain metabolites in the blood and the likelihood that a dosage of a thiopurine drug will prove ineffective or cause harm'. They asked, 'do the patent claims add enough to their statements of the correlations to allow the processes they describe to qualify as patent-eligible processes that apply natural laws?' \[9\].

Their unanimous answer was that in this case, the aspects of the claims related to application of the law of nature were 'not enough'. Supreme Court decisions are supposed to guide the lower judiciary and lawyers' expectations for subsequent cases, but it is unclear just how to determine whether similar method claims might pass muster under this analysis. That is, how can the Supreme Court's reasoning be applied to other patents to reasonably predict how much is enough? Without further guidance from the Supreme Court, method claims regarding specific genes are now in a tenuous position.

Moreover, the Supreme Court's analysis discusses that the steps of the claimed process are 'well understood, routine, conventional activity'. This sounds to the patent community suspiciously close to a line of argument about novelty or obviousness rather than about proper subject matter eligibility \[10\]. Alternatively, it might suggest that the Supreme Court is insisting that novelty and obviousness have some relevance to subject matter eligibility. The CAFC has now asked the parties to submit supplemental briefs explaining how the *Prometheus* ruling applies to the *Myriad* claims.

# Isolated DNA molecules

Although broad method claims may have the potential for widespread effects on the genetic community, patent claims on any particular isolated DNA molecule, although common, are becoming less relevant in diagnostics because of advancing technology. Next-generation sequencing platforms generally avoid the need to isolate any particular DNA molecule. It is therefore unlikely that sequencing would infringe claims requiring DNA isolation, including a claim to an isolated DNA having any particular sequence. We are not the first to propose that whole-genome sequencing probably infringes patents involving genetic technologies to a lesser extent than widespread beliefs might suggest \[11\].

There is further reason to be less concerned about product claims that focus on isolated DNA. Owing to the dissemination of genetic knowledge and requirements of latter sections of the Patent Act (Box 1), it is becoming more difficult to receive allowance from the patent office on DNA claims. If the sequence has been published, such as in the reference human genome, the corresponding DNA molecules become far less novel and non-obvious; novelty and non-obviousness are both additional requirements for patenting. Moreover, the patents having DNA claims in the *Myriad* case will begin to expire in 2014, and many other patents covering DNA molecules with naturally occurring sequences will likewise expire in the next decade.

The Supreme Court has stated that Congress intended patentable subject matter to 'include anything under the sun that is made by man' \[12\], and it is indisputable that a PCR product fits that description. A gene as it exists in nature, by contrast, is certainly not 'made by man', and enforcement of the Myriad method claim discussed above could arguably monopolize the *BRCA2* gene to some extent. To be fair, holders of such gene patents have generally not used them against basic research activities. One only has to search 'BRCA' in literature databases to realize that Myriad has not asserted its patents against publicly funded basic research endeavors. Nevertheless, these claims have been asserted in the setting of clinical research for a fee by Myriad Genetics, and indiscriminate assertion of such patents is always a possibility.

Although granting patent protection related to a naturally occurring gene is both fundamentally problematic to some and increasingly dubious from the standpoint of novelty and non-obviousness to others, the development of new, non-natural DNA sequences through synthetic biology is a field in which full public disclosure in patent applications is highly desirable and warrants rewarding the inventor with a monopoly. So for synthetic DNA technology, it should be permissible to claim a DNA having a truly novel sequence, because there is no DNA with that sequence found in nature.

# More medical diagnostic claims: mental steps and abstract concepts

The *Myriad* case has received the most attention in recent popular media, and *Prometheus* is now running a close second. But there is a third relevant contemporary biotechnology case involving diagnostic methods claims in which the challenge is focused solely on whether the subject matter is patent eligible. The common thread among these cases is the primary issue argued - that the methods claims are 'abstract'. A judicially created doctrine forbids patenting of abstract principles \[8\].

It can be very challenging for the courts to determine where to draw the line between a purely abstract idea that effectively 'preempts broadly the use of any natural correlation' and a patentable application of that concept, such as a specific biochemical test \[13\]. For example, well-known methods used to administer prescription drugs and test blood have been found to involve 'transformations' of body chemistry, which moves the method out of the abstract category \[14\].

*Classen Immunotherapies, Inc. v. Biogen Idec* ('*Classen*'), like *Prometheus*, is also not specifically about gene patents but involves conceptually similar method claims \[13\]. In this case, the *Classen* patents cover methods for correlating vaccination schedules with later development of immune-mediated diseases. Some of the claims are very broad. In fact, Judge Moore dissented from the other two judges in the ruling, and she read the claims to preempt the scientific method itself. The basic steps of the method are:

\(1\) screening information on immunization schedules and the occurrence of chronic immune-mediated disease,

\(2\) comparing the results from different vaccination schedules and identifying the lower risk schedule, and

\(3\) administering the vaccine on that lower risk schedule.

The CAFC invalidated some claims that only include steps of screening and comparing vaccination schedules for correlations with later development of immune-mediated disorders. However, once again the 'administering' step takes the claims out of the abstract category according to the majority of judges. The court allowed claims that included an additional step of administering vaccinations, solely because the additional administering step transforms the method from an abstract concept to a patent-eligible application of that concept. The striking effect, as noted by the CAFC, is that a physician might infringe merely by reviewing the literature and then vaccinating on the schedule that seems best to him or her.

A common theme in earlier higher court decisions is that a method claim solely comprising mental steps such as reviewing, comparing, analyzing, screening, selecting, and determining will not stand as eligible to be patented because it is abstract. And even if the claim includes additional non-abstract steps, such as in *Classen* and *Prometheus*, it may not stand if it does not meet the requirements of the rest of the Patent Act, such as novelty and non-obviousness.

# Novelty and non-obviousness

Novelty is a question of whether the technology is really new. Obviousness may be found where the 'subject matter as a whole... was obvious to one of ordinary skill in the art...' at the time of invention \[15\]. This is usually an inquiry that involves having experts from each party explain why their party is correct to advise the court on how obvious they think the invention was. This is also a difficult inquiry, but at least the courts are instructed to consider a number of better-defined criteria that come from precedential cases (called *Graham* factors).

The CAFC and many patent practitioners have expressed frustration over the recent trend of increased reliance on subject matter eligibility arguments (such as whether the patent claim is abstract) rather than novelty or obviousness arguments \[16\]. The patent community has long struggled to properly convey this important concept - the sections that follow section 101 (Box 1) are meant to provide the real teeth for eliminating objectionable patents. For example, according to a precedential patent office case (affirmed by the CAFC), a claim to the human DNA for protein × is obvious if the mouse DNA for protein × was already known in the literature and there was good reason to believe that homologous human DNA existed \[17\]. The Supreme Court analysis of *Prometheus* sounds disturbingly similar to an obviousness argument to many patent practitioners \[10\]. Thus, it is not clear that the present Supreme Court fully understands the distinctions between section 101 and the sections that follow it, which have historically been preferred over section 101 for finding problematic subject matter unpatentable.

A general tenet of US law holds that, where multiple arguments are presented, the court focuses on the clearest and most determinative argument that can decide the case. In the interest of judiciary efficiency, the court does not consider other more difficult issues. It is much easier for judges to explain a decision that invalidates a claim based on arguments that the invention is obvious or because it has already been described in the literature than decisions based on other issues. The Supreme Court and CAFC both previously tended towards that strategy, to avoid addressing the more difficult and more broadly applicable section 101 subject matter question that is designated to Congress. And Congress does act on that responsibility occasionally - the 2011 America Invents Act includes a prohibition on patents covering human organisms \[18\].

The arguments available under the latter sections of the Patent Act are often much stronger and clearer. In fact, in *Classen* the CAFC took the unusual additional step of outlining exactly how all of the remaining 'administering' claims would have been successfully defeated under patent rules in other parts of the Act. The most glaring issue was that the literature had completely described a system for comparing vaccination schedules over a decade before Classen tried to patent the method, so the invention lacked novelty and would therefore be invalidated if challenged on that theory.

In addition, in *Classen* the CAFC panel specifically admonishes litigants over the lack of arguments based on other parts of the Patent Act, in three separate opinions. All three judges suggest to varying degrees that all of *Classen*'s claims would be invalid if properly challenged. Indeed, even in the *Myriad* decision the CAFC suggests that arguments under the more stringent sections of the Patent Act would have a much better chance of defeating even the isolated DNA claims.

# Other important factors to consider

Each claim in a patent may stand or fall independently of the other claims. Thus, each claim in a patent must be analyzed separately to determine what it encompasses. The examiners at the patent office are charged to do the same analysis before granting a patent, but as with all human activity, it is inevitable that examiners make mistakes. A safeguard lies in the fact that US patents can be challenged after they are granted on most of the same requirements as for examination.

Secrecy is sometimes an alternative to patenting, but one that conveys less benefit to the public than the protection of technology investment through granting of patents. The public value of the patent system lies in the innovator's duty to fully disclose the patented discovery or invention to the public in return for the limited monopoly granted by a patent. In addition, patents serve to provide security to potential investors in new biotechnologies.

The possibility that privately funded ventures will increasingly resort to secrecy to protect new DNA technologies is substantial and could frustrate progress overall. Some types of technology will not be feasible to protect through secrecy, and those potential ventures may simply be abandoned for lack of confidence in the ability to protect investment. Thus, it can be dangerous to take an all-or-none approach to the patent system and its effects on progress in medical science. Licensing practices are also important in limiting or exacerbating negative effects of patents. In particular, those involved in technology transfer of federally funded university inventions should be careful in appropriate circumstances to maintain policies against exclusive licensing (\[19\] and Catherine Innes, personal communication).

History is full of examples in which existing law could not keep up with what was happening on the ground. The Homestead Act of 1862 was an official acknowledgement by the US government of the pioneers' land claims, but people were moving west, staking those claims, and occupying the land before the Homestead Act anyway. Likewise, we are probably witnessing significant patent infringement (with regard to all of the now-tenuous method claims) owing to the reality of next-generation sequencing and its widespread adoption. Science forges ahead despite the more broadly drafted method claims, including the myriad large-scale sequencing efforts (pun intended) currently underway at multiple centers. Such efforts are potentially infringing many method patents that may or may not withstand legal challenge according to the Supreme Court's fuzzy analysis of *Prometheus*. Meanwhile, stakeholders will benefit from considering what activities are truly preempted by specific types of patent claims.

Finally, it is important to remember the central intent of the US patent system, which is not primarily as a generator of revenue but rather to 'promote the progress of science and useful arts'. Broad patent claims covering diagnostic methods should be a greater concern for proponents of unencumbered scientific exploration and medicine than the 'isolated DNA' product claims more commonly associated with the term 'gene patent'. The medical and genetic communities should pay attention to what really matters.

# Competing interests

The authors declare that they have no competing interests.

# Box 1. A US patent law primer

US patent law requires that a number of different complex requirements be met before a patent is granted, and the validity of an issued patent may be challenged thereafter on any of the same grounds. The requirements are codified in sections 101, 102, 103, and 112 of USC Title 35, also known as the Patent Act. Section 101 addresses only what types of subject matter are eligible for patenting \[5\]; the other sections address issues of novelty, obviousness, and sufficiency of the accompanying descriptions for fully enabling others in the field to make and use the invention or discovery.

Historically, section 101 has been construed very broadly by the Supreme Court and the US Patent and Trademark Office to include 'anything under the sun made by man' and the other sections have been applied more stringently to filter out subject matter that is unsuitable for patenting (for example, products of nature and abstract concepts). The distinctions between the requirements of these four sections of the Patent Act are the source of much confusion for many non-patent professionals, including scientists, engineers, physicians, and even lawyers and judges who are not specialists in patent law.

A US patent is enforceable only in its home country. However, international agreements such as the Patent Cooperation Treaty provide some degree of uniformity between the patent laws of various countries \[20\]. These agreements also provide for easier pursuit of patent protection on a new technology in multiple jurisdictions. Nevertheless, the courts of each country must interpret the particular law of their own jurisdiction, and all of these factors combined yield different end results for patent challenges across the world \[21,22\].

The Court of Appeals for the Federal Circuit (CAFC) is the highest patent specialist court in the US. In fact, this court of appeals has unique authority among all the US courts of appeals. Its decisions are binding precedents throughout the US on patent law matters, whereas the other courts of appeals are binding only for their own geographic jurisdictions. Thus, the only superseding authorities are the Supreme Court and Congress. Furthermore, because Supreme Court review is discretionary, CAFC decisions are often the final word on patent law matters \[23\].


abstract: # Background
  .
  Plan–do–study–act (PDSA) cycles provide a structure for iterative testing of changes to improve quality of systems. The method is widely accepted in healthcare improvement; however there is little overarching evaluation of how the method is applied. This paper proposes a theoretical framework for assessing the quality of application of PDSA cycles and explores the consistency with which the method has been applied in peer-reviewed literature against this framework.
  .
  # Methods
  .
  NHS Evidence and Cochrane databases were searched by three independent reviewers. Empirical studies were included that reported application of the PDSA method in healthcare. Application of PDSA cycles was assessed against key features of the method, including documentation characteristics, use of iterative cycles, prediction-based testing of change, initial small-scale testing and use of data over time.
  .
  # Results
  .
  73 of 409 individual articles identified met the inclusion criteria. Of the 73 articles, 47 documented PDSA cycles in sufficient detail for full analysis against the whole framework. Many of these studies reported application of the PDSA method that failed to accord with primary features of the method. Less than 20% (14/73) fully documented the application of a sequence of iterative cycles. Furthermore, a lack of adherence to the notion of small-scale change is apparent and only 15% (7/47) reported the use of quantitative data at monthly or more frequent data intervals to inform progression of cycles.
  .
  # Discussion
  .
  To progress the development of the science of improvement, a greater understanding of the use of improvement methods, including PDSA, is essential to draw reliable conclusions about their effectiveness. This would be supported by the development of systematic and rigorous standards for the application and reporting of PDSAs.
author: Michael J Taylor; Chris McNicholas; Chris Nicolay; Ara Darzi; Derek Bell; Julie E ReedCorrespondence to Michael J Taylor, Academic Surgical Unit, 10th Floor, QEQM building, St Mary's Hospital, Paddington, London W2 1NY, UK; <email@example.com>
date: 2014-04
institute: 1Department of Surgery and Cancer, Imperial College London, London, UK; 2National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for North-West London, London, UK
references:
title: Systematic review of the application of the plan–do–study–act method to improve quality in healthcare

# Introduction

Delivering improvements in the quality and safety of healthcare remains an international challenge. In recent years, quality improvement (QI) methods such as plan–so–study–act (PDSA) cycles have been used in an attempt to drive such improvements. The method is widely used in healthcare improvement; however there is little overarching evaluation of how the method is applied. This paper proposes a theoretical framework for assessing the quality of application of PDSA cycles and explores the quality and consistency of PDSA cycle application against this framework as documented in peer-reviewed literature.

## Use of PDSA cycles in healthcare

Despite increased investment in research into the improvement of healthcare, evidence of effective QI interventions remains mixed, with many systematic reviews concluding that such interventions are only effective in specific settings.1–4 To make sense of these findings, it is necessary to understand that delivering improvements in healthcare requires the alteration of processes within complex social systems that change over time in predictable and unpredictable ways.5 Research findings highlight the influential effect that local context can have on the success of an intervention6 7 and, as such, 'single-bullet' interventions are not anticipated to deliver consistent improvements. Instead, effective interventions need to be complex and multi-faceted8–11 and developed iteratively to adapt to the local context and respond to unforeseen obstacles and unintended effects.12 13 Finding effective QI methods to support iterative development to test and evaluate interventions to care is essential for delivery of high-quality and high-value care in a financially constrained environment.

PDSA cycles provide one such method for structuring iterative development of change, either as a standalone method or as part of wider QI approaches, such as the Model for Improvement (MFI), Total Quality Management, Continuous QI, Lean, Six Sigma or 'Quality Improvement Collaboratives'.3 4 14 Despite increased use of QI methods, the evidence base for their effectiveness is poor and under-theorised.15–17 PDSA cycles are often a central component of QI initiatives, however few formal objective evaluations of their effectiveness or application have been carried out.18 Some PDSA approaches have been demonstrated to result in significant improvements in care and patient outcomes,19 while others have demonstrated no improvement at all.20–22

Although at the surface level these results appear disheartening for those involved in QI, there is a need to explore the extent to which the PDSA method has been successfully deployed to draw conclusions from these studies. Rather than see the PDSA method as a 'black box' of QI,23 it is important to understand that the use of PDSA cycles is, itself, a complex intervention made up of a series of interdependent steps and key principles that inform its application5 24 25 and that this application is also affected by local context.26 To interpret the results regarding the outcome(s) from the application of PDSA cycles (eg, whether processes or outcomes of care improved) and gauge the effectiveness of the method, it is necessary to understand how the method has been applied.

No formal criteria for evaluating the application or reporting of PDSA cycles currently exist. It is only in recent years, through SQUIRE guidelines, that frameworks for publication have been developed that explicitly consider description of PDSA application.27 28 We consider that such criteria are necessary to support and assess the effective application of PDSA cycles and to increase their legitimacy as a scientific method for improvement. We revisited the origins and theory of the method to develop a theoretical framework to evaluate the application of the method.

## The origins and theory of PDSA cycles

The PDSA method originates from industry and Walter Shewhart and Edward Deming's articulation of iterative processes which eventually became known as the four stages of PDSA.25 PDCA (plan–do–check–act) terminology was developed following Deming's early teaching in Japan.29 The terms PDSA and PDCA are often used interchangeably in reference to the method. This distinction is rarely referred to in the literature and for the purpose of this article we consider PDSA and PDCA but refer to the methodologies generally as 'PDSA' cycles unless otherwise stated.

Users of the PDSA method follow a prescribed four-stage cyclic learning approach to adapt changes aimed at improvement. In the 'plan' stage a change aimed at improvement is identified, the 'do' stage sees this change tested, the 'study' stage examines the success of the change and the 'act' stage identifies adaptations and next steps to inform a new cycle. The MFI30 and FOCUS31 (see figure 1) frameworks have been developed to precede the use of PDSA and PDCA cycles30 31 respectively (table 1).

Description of the plan–do–study–act (PDSA) cycle method according to developers and commentators

Deming (1986)25Original description of the method relating to manufacturing
Langley (1996)30How the PDSA method may be adapted for use in healthcare contexts
Speroff and O'Connor (2004)33How the PDSA method is analogous to scientific methodology

Plan
Plan a change or test aimed at improvement

Identify objective

Identify questions and predictions

Plan to carry out the cycle (who, when, where, when)

Formation of a hypothesis for improvement

Do
Carry out the change or test (preferably on a small scale)

Execute the plan

Document problems and unexpected observations

Begin data analysis

Conduct study protocol with collection of data

Study
Examine the results. What did we learn? What went wrong?

Complete the data analysis

Compare data to predictions

Summarise what was learnt

Analysis and interpretation of the results

Act
Adopt the change, abandon it or run through cycle again

What changes are to be made?

What will the next cycle entail?

Iteration for what to do next

In comparison to more traditional healthcare research methods (such as randomised controlled trials in which the intervention is determined in advance and variation is attempted to be eliminated or controlled for), the PDSA cycle presents a pragmatic scientific method for testing changes in complex systems.32 The four stages mirror the scientific experimental method33 of formulating a hypothesis, collecting data to test this hypothesis, analysing and interpreting the results and making inferences to iterate the hypothesis.

The pragmatic principles of PDSA cycles promote the use of a small-scale, iterative approach to test interventions, as this enables rapid assessment and provides flexibility to adapt the change according to feedback to ensure fit-for-purpose solutions are developed.10 12 13 Starting with small-scale tests provides users with freedom to act and learn; minimising risk to patients, the organisation and resources required and providing the opportunity to build evidence for change and engage stakeholders as confidence in the intervention increases.

In line with the scientific experimental method, the PDSA cycle promotes prediction of the outcome of a test of change and subsequent measurement over time (quantitative or qualitative) to assess the impact of an intervention on the process or outcomes of interest. Thus, learning is primarily achieved through interventional experiments designed to test a change. In recognition of working in complex settings with inherent variability, measurement of data over time helps understand natural variation in a system, increase awareness of other factors influencing processes or outcomes, and understand the impact of an intervention.

As with all scientific methods, documentation of each stage of the PDSA cycle is important to support scientific quality, local learning and reflection and to ensure knowledge is captured to support organisational memory and transferability of learning to other settings.

This review examines the application of PDSA cycles as determined by these principle features of the PDSA method described above. We recognise that a number of health and research related contextual factors may affect application of the method but these factors are beyond the scope of this review. The review intends to improve the understanding of whether the PDSA method is being used and reported in line with the literature informed criteria and therefore inform the interpretation of studies that have used PDSA cycles to facilitate iterative development of an intervention.

# Methods

A systematic narrative review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.34

## Search

The search was designed to identify peer-reviewed publications describing empirical studies that applied the PDSA method. Taking into account the development of the method and terminology, the search terms used were 'PDSA', 'PDCA', 'Deming Cycle', 'Deming Circle', 'Deming Wheel' and 'Shewhart Cycle'. No year of publication restrictions were imposed.

## Information sources

The following databases were searched for articles: Allied and Complementary Medicine Database (AMED; 1985 to present), British Nursing Index (BNI; 1985 to present), Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1981 to present), Embase (1980 to present), Health Business Elite (EMBESCO Publishing, Ipswich, Massachusetts, USA), the Health Management Information Consortium (HMIC), MEDLINE from PubMed (1950 to present) and PsychINFO (1806 to present) using the NHS Evidence online library (REF), and the Cochrane Database of Systematic Reviews. The last search date was 25 September 2012.

## Data collection process and study selection

Data were collected and tabulated independently by MJT, CM and CN in a manner guided by the Cochrane Handbook. Eligibility was decided independently, in a standardised manner and disagreements were resolved by consensus. If an abstract was not available from the database, the full-text reference was accessed.

Inclusion criteria for articles were as follows: published in peer-reviewed journal; describes PDSA method being applied to improve quality in a healthcare setting; published in English. Editorial letters, conference abstracts, opinion and audit articles were excluded from the study selection.

## Data items

A theoretical framework was constructed by compartmentalising the key features of the PDSA method into observable variables for evaluation (table 2). This framework was developed in accordance with recommendations for PDSA use cited in the literature, describing the origins and theory of the method. Face validity of the framework was achieved through discussion among authors, with QI facilitators and at local research meetings.

Theoretical framework based on key features of the plan–do–study–act (PDSA) cycle method

Feature of PDSA
Description of feature
How this was measured

Iterative cycles
To achieve an iterative approach, multiple PDSA cycles must occur. Lessons learned from one cycle link and inform cycles that follow. Depending on the knowledge gained from a PDSA cycle, the following cycle may seek to modify, expand, adopt or abandon a change that was tested

Were multiple cycles used?

Were multiple cycles linked to one another (ie, does the 'act' stage of one cycle inform the 'plan' stage of the cycle that follows)?

When isolated cycles were used were future actions postulated in the 'act' stage?

Prediction-based test of change
A prediction of the outcome of a change is developed in the 'plan' stage of a cycle. This change is then tested and examined by comparison of results with the prediction

Was a change tested?

Was an explicit prediction articulated?

Small-scale testing
As certainty of success of a test of change is not guaranteed, PDSAs start small in scale and build in scale as confidence grows. This allows the change to be adapted according to feedback, minimises risk and facilitates rapid change and learning

Sample size per cycle?

Temporal duration of cycles?

Number of changes tested per cycle?

Did sequential cycles increase scale of testing?

Use of data over time
Data over time increases understanding regarding the variation inherent in a complex healthcare system. Use of data over time is necessary to understand the impact of a change on the process or outcome of interest

Was data collected over time?

Were statistics used to test the effect of changes and/or understand variation?

Documentation
Documentation is crucial to support local learning and transferability of learning to other settings

How thoroughly was the application of the PDSA method detailed in the reports?

Was each stage of the PDSA cycles documented?

Data were collected regarding application of the PDSA method in line with the theoretical framework. Other data collected included first author, year of publication, country, area of healthcare, use of PDSA or PDCA terminology, and use of MFI or FOCUS as supporting frameworks. Ratios were used to analyse the results regarding the majority of variables, and mean scores regarding data associated with length of study, length of PDSA cycle and sample size were also used for analysis. Data were analysed independently by MJT and CM. Discrepancies (which occurred in less than 3% of data items) were resolved by consensus.

## Risk of bias in individual studies

The present review aimed to assess the reported application of the PDSA method and the results of individual studies were not analysed in this review.

## Risk of bias across studies

Despite our review being focused on reported application, rather than success of interventions, it may still be possible that publication bias affected the results of this study. Research that used PDSA methodology, but did not yield successful results, may be less likely to get published than reports of successful PDSA interventions.

# Results

## Study selection

A search of the databases yielded 942 articles. After removal of duplicates, 409 remained; 216 and 120 were further discarded following review of abstracts and full texts, respectively. Excluded articles did not apply the PDSA method as part of an empirical study or coincidently used the acronyms PDSA or PDCA for different terms, or were abstracts for conferences or poster presentations. A total of 73 articles met the inclusion criteria and were included in the review (see figure 2).

## General study characteristics

### Country of study

The retrieved articles describe studies conducted in the USA (n=46), the UK (n=13), Canada (n=3) Australia (n=3), the Netherlands (n=2) and one each from six other countries (see online supplementary appendix A for complete synthesis of results).

### Healthcare discipline to which method was applied

This varied across acute and community care and clinical and organisational settings. The most common settings were those of pain management and surgery (six articles each).

### Method terminology

Of the 73 articles identified, 42 articles used 'PDSA' as terminology and 31 referred to the method as 'PDCA'. Eight of these reported using the MFI. Thirty-one articles used 'PDCA' terminology, with 20 using the preceding FOCUS framework. One article described use of FOCUS and MFI. Over time there was an increase in the prevalence of PDSA use with PDCA use diminishing (see online supplementary figure S1). The earliest reported use of PDCA and PDSA in healthcare was 1993 and 2000, respectively.

## Documentation

The following four categories were used to describe the extent to which cycles were documented in articles (n=73): no detail of cycles (n=16); themes of cycles (but no additional details) (n=8); details of individual cycles, but not of stages within cycles (n=8); details of cycles including separated information on stages of cycles (n=41).

Analysis of articles against the developed framework was dependent on the extent to which the application of PDSA cycles was reported. Articles that provided no details of cycles or only themes of cycles were insufficient for full review and excluded for analysis against all features. Articles that provided further details of cycles completed (n=49) were included for analysis against the remaining four features of the framework. A full breakdown of findings can be viewed in online supplementary appendix B.

## Application of method

### Iterative cycles (n=49)

Fourteen articles described a sequence of iterative cycles (two or more cycles with lessons learned from one cycle linking and informing a subsequent cycle), 33 described isolated cycles that are not linked, and 2 articles described cycles that used PDSA stages in the incorrect order (in one article, one plan, one do, two checks and three acts were described, PDACACA35; a further study did not report use of a 'check' stage; PDA36) and are excluded from further review. Of the 33 articles that described non-iterative cycles, 29 reported a single cycle being used, and 4 described multiple, isolated (non-sequential) cycles. Although future actions are often suggested in articles that reported a single cycle, only three explicitly mentioned the possibility of further cycles taking place. A total of 13.6% (3/22) of PDCA studies described the application of iterative cycles compared with 44% (11/25) of PDSA studies describing the application of iterative cycles (see figure 3).

### Prediction-based testing of change (n=47)

The aims of the cycles adhered to one of two themes: tests of a change; and collection or review of data without a change made. Of the 33 articles with single cycles, 30 aimed to test a change while 3 used the PDSA method to collect or review data. Of the 14 articles demonstrating sequential cycle use, 8 solely used their cycles to test change whilse5 began with a cycle collecting or reviewing data followed by cycles testing change. One article described a mixture of cycles testing changes and cycles that involved collection/review of data. Four of the 47 studies contained an explicit prediction regarding the outcome of a change; all 4 aimed to test a change (see online supplementary table S1).

### Small-scale testing (n=47)

Scale was assessed in three ways: sample size, duration and complexity. Sample size refers to quantity of observations used to measure the change; duration refers to the length of PDSA cycle application; and complexity refers to the quantity of changes administered per cycle.

#### Sample size

Patient data, staff data and case data were used as samples within PDSA cycles. Twenty-seven articles reported a sample size from at least one of their cycles. Twenty-one of these were isolated cycle studies with sample size ranging from 7 to 2079 (mean=323.33, SD=533.60). The remaining six studies reporting individual cycle sample sizes used iterative cycles; the sample size of the first cycles of these ranged from 1 to 34 (mean=16.75, SD=11.47). Two of these studies described the use of incremental sample sizes across cycles, three used non-incremental sample sizes across cycles, and one changed the type of sample. Of the eight iterative cycle articles that did not report individual cycle sample sizes, two did not differentiate sample sizes between cycles and instead gave an overall sample for the chain of cycles and six did not report sample size.

#### Duration

Reported *s*tudy duration of isolated cycles ranged from 2 weeks to 5 years (mean=11.91 months, SD=12.81). Only five articles describing iterative cycles explicitly reported individual cycle duration. Individual cycle duration could be estimated from the total duration of the PDSA cycle chain and the number of cycles conducted, resulting in approximate cycle lengths ranging from three cycles in 1 day to one cycle in 16 months (mean=5.41 months, SD=4.80, see online supplementary figure S2). The total PDSA cycle duration for series of iterative cycles (first to last cycle of one chain) ranged from 1 day to 4 years (mean=20.38, SD=20.39 months).

#### Complexity

Twenty-two articles reported more than one change being tested within a single cycle. Of the articles describing iterative cycles, 42% administered more than one change per cycle compared with 48% of the articles describing non-iterative PDSA cycles.

### Data over time (n=47)

All studies used a form of qualitative and quantitative data to assess cycles. Studies were categorised according to four types of reporting quantitative data: regular (n=15), three or more data points with consistent time intervals; non-regular (n=16), before and after or per PDSA cycle; single data point (n=8), a single data point after PDSA cycle(s); and no quantitative data reported (n=8). Of the 15 articles that used regular data, only 7 used monthly or more frequent data intervals (see online supplementary figure S3 for full frequency of regular quantitative data reporting). No studies reported using statistical process control to analyse data collected from PDSA cycles. Eleven included analysis of data using inferential statistical tests (five of these studies collected isolated data, six involved continuous data collection).

Of the eight articles that did not report any quantitative data, two reported that quantitative analyses had taken place but did not present the findings and six described the use of qualitative feedback only (one non-regular, five single data point). Qualitative data were gathered through a range of mechanisms from informal staff or patient feedback to structured focus groups.

# Discussion

PDSA cycles offer a supporting mechanism for iterative development and scientific testing of improvements in complex healthcare systems. A review of the historic development and rationale behind PDSA cycles has informed the development of a theoretical framework to guide the evaluation of PDSA cycles against use of iterative cycles, initial small-scale testing, prediction-based testing of change, use of data over time and documentation.

Using these criteria to assess peer-reviewed publications of PDSA cycles demonstrates an inconsistent approach to the application and reporting of PDSA cycles and a lack of adherence to key principals of the method. Only 2/7337 38 articles demonstrated compliance with criteria in all five principles. Assessment of compliance was problematic due to the marked variation in reporting of this method, which reflects a lack of standardised reporting requirements for the PDSA method.

From the articles that reported details of PDSA cycles it was possible to ascertain that variation is inherent not just in reporting standards, but in the conduct of the method, implying that the key principles of the PDSA method are frequently not followed. Less than 20% (14/73) of reviewed articles reported the conduct of iterative cycles of change, and of these, only 15% (2/14) used initial small-scale tests with increasing scale as confidence in the intervention developed. These results suggest that the full benefits of the PDSA method would probably not have been realised in these studies. Without an iterative approach, learning from one cycle is not used to inform the next cycle, and therefore it is unlikely that interventions will be adapted and optimised for use in a particular setting. Furthermore, large-scale cycles risk significant resource investment in an intervention that has not been tested and optimised within that environment and risk producing 'false' negatives.

Only 14% (7/47) of articles reported use of regular data over time at monthly or more frequent intervals, indicating a lack of understanding around the use of the PDSA method to track change within a 'live' system, and limiting the ability to interpret the results from the study. Cycles that included an explicit prediction of outcomes were reported in only 9% (4/47) of articles, suggesting that PDSA cycles were not used as learning cycles to test and revise theory-based predictions.

Overall these results demonstrate poor compliance with key principles of the PDSA method, suggesting that it is not being used optimally. The increasing trend in using PDSA (as opposed to 'PDCA') cycles in recent years, however, does seem to have been accompanied by an increase in compliance with some key principles, such as use of iterative cycles. Deming was cautious over the use of the 'PDCA' terminology and warned it referred to an explicitly different process, referring to a quality control circle for dealing with faults within a system, rather than the PDSA process, which was intended for iterative learning and improvement of a product or a process.39 This subtle difference in terminologies may help to explain the better compliance with key methodological principles in studies that refer to the method as 'PDSA'.

One of the articles identified in the search included comments by the authors that the PDSA method should be 'more realistically represented',40 as ineffective cycles can be 'abandoned' early on, making it needless to go through all four stages in each iteration. These comments may provide insight into an important potential misunderstanding of the PDSA methodology. Ineffective changes will result in learning, which is a fundamental principle behind a PDSA cycle. However minor this abandoned trial may have been, it can still be usefully described as a PDSA cycle. A minor intervention may be planned (P) and put into practice (D). A barrier may be encountered (S), resulting in a decision being made to retract the intervention, and to do something differently (A).

The theoretical framework presented in this paper highlights the complexity of PDSA cycles and the underpinning knowledge required for correct application. The considerable variation in application observed in the reported literature suggests that caution should be taken in interpreting results from evaluations in which PDSAs are used in a controlled setting and as a 'black box' of QI. This review did not compare the effectiveness of use to reported outcomes and therefore this study does not conclude whether better application of the PDSA method results in better outcomes, but instead draws on theoretical principles of PDSAs to rationalise why this would be expected. Prospective mechanistic studies exploring the effective application of the method as well as study outcomes would be of greater use in drawing conclusions regarding the effectiveness of the method. The framework presented in this paper could act as a good starting point for such studies.

The fact that only peer-reviewed publications were assessed in this study means that results may be affected by publication bias. This is anticipated both in terms of what is accepted for publication but also the level and type of detail that is requested and allowed in typical publications (eg, before and after studies are more common than presenting data over time and this may make these types of studies easier to publish). Though QI work may be easier to publish now through recent changes in publication guidelines,27 possible publication outlets continue to be relatively limited.

To support systematic reporting and encourage appropriate usage, we suggest that reporting guidelines be produced for users of the PDSA method to increase transparency as to the issues that were encountered and how they were resolved. While PDSA is analogous to a scientific method, it appears to be rarely used or reported with scientific rigour, which in turn, inhibits perceptions of PDSA as a scientific method. Such guidelines are essential to increase the scientific legitimacy of the PDSA method as well as to improve scientific rigour or application and reporting. Although the SQUIRE guidelines make reference to the potential use of PDSA cycles, further support to users and teachers, and publication of this improvement method seems necessary. Consistent reporting of PDSA structure would allow meta-evaluation and systematic reviews to further build the knowledge of how to use such methods effectively and the principles to apply to increase chances of success.

It is clear from these findings that there is much room for improvement in the application and use of the PDSA method. Previous studies have discussed the influence of different context factors on the use of QI methods, such as motivation, data support infrastructure and leadership20 22 41–43 Understanding how high-quality usage can be promoted and supported needs to become the focus of further research if such QI methods are going to be used effectively in mainstream healthcare.

# Conclusions

There is varied application and reporting of PDSAs and lack of compliance with the principles that underpin its design as a pragmatic scientific method. The varied practice compromises its effectiveness as a method for improvement and cautions against studies that view QI or PDSA as a 'black box' intervention.

There is an urgent need for greater scientific rigour in the application and reporting of these methods to advance the understanding of the science of improvement and efficacy of the PDSA method. The PDSA method should be applied with greater consistency and with greater accordance to guidelines provided by founders and commentators25 30 44 45

# Supplementary Material

###### Web appendix A

###### Web appendix B

###### Web figures

The authors would like to thank Dr Thomas Woodcock for his valuable input into the theoretical framework and data analysis.

# References