A summary of the early phases of the glaucoma surgical treatment.
\r\n\tAs an analytical technique with realms of applications, EIS has seen major progress during the past few years. One reason could be the feasibility of implementing EIS in a system, and the other would be the usefulness of EIS data in determining properties such as reaction rate, as well as the diffusion coefficients. Applications of EIS varied between corrosion analysis and inhibition, food and drug analysis, monitoring the performance of batteries, and developing biosensors, etc. Impedance microbiology which is used to monitor bacterial growth in a sample, and tissue electrical impedance which is basically used for detecting abnormalities in morphology and health of tissues, are other examples for the applications of EIS.
At the present time it is much easier to recognize and to assess glaucomatous changes at the optic nerve than it used to be. This is possible thanks to modern devices and imaging techniques that allow much faster and better diagnosing. Even today, the single most important thing in this matter is to know the characteristics of the normal -healthy optic disc (Figure 1.). The appearance of the optic disc, as in the other biological variables varies widely among healthy individuals. This fact complicates the recognition of the pathological changes.
Today modern glaucoma diagnostic is unimaginable without technological support, when it comes to discovering as well as for following up glaucoma optic neuropathy.
With standard clinical exam aside, there is a number of imaging devices that we use in everyday practice, and to mention a couple i.e. CVF, HRT, GDX, OCT, PACHIMETRY, FUNDUS PHOTOS, CDI… and we agree that without the help of this wide technological spectrum of supporting diagnostic devices we could not be able to diagnose the disease or to track the glaucoma changes. Just stop for a second and remember how it was in the old days? Let’s take a glance of the old days and how it all started?
There was the time when ophthalmologist did not have those sophisticated imaging devices; they even did not have slit lamps… despite the fact that they were glaucomatologists!
This chapter is dedicated to the pioneers of ophthalmology and glaucomathology; their legacy for future glaucomatologists.
The term optic disc is frequently used to describe the portion of the optic nerve clinically visible on examination. This, however, may be slightly inaccurate as ‘disc’ implies a flat, 2 dimensional structure without depth, when in fact the ‘optic nerve head’ is very much a 3 dimensional structure which should ideally be viewed stereoscopically.
Every disease has its history, as much in diagnosing-discovering it, as in quality and adequate treatment. History of the diseases categorized today under the term “glaucoma” may be divided into three major periods. First period is the earliest and it stretches from approximately 400 BC up until 1600 AD; during the course of this period the term “glaucoma” was used to refer to a general group of blinding ocular diseases without the distinctions that historians now can recognize. During the middle period from the beginning of the 17th century to the middle of the 19th century the cardinal signs of glaucoma, separately and in combination, were described in published texts. Finally, the third period starts with the introduction of the ophthalmoscope (Helmholtz, 1854) to the present.
First period (400 BC to 1600 AD)
Etymology of the term glaucoma is that it derives from the Greek word ‘‘glaukos’’, which appears in the Homer’s notes, where it is mentioned as -a sparkling silver glare, later used for colours such as sky-blue or green. As a diagnosis by physicians, glaucoma is first mentioned in Hippocrates\' Aphorisms (Figure 2.),lists among the infirmities of the aged a condition he called “glaucosis” which he associated with “dimness of vision”. Later Aristotel did not mention any diseases called glaucoma particularly, although he helped create the foundation for research into the pathology of the disease, thus giving his contribution to early glaucoma research.
It is interesting that most authors, by the Roman era, used the term glaucoma for what is now known as cataract. For example, Oribasius (325-400 AD) quotes Ruphus from Ephesus (1st century AD) as using the term for “that condition of the crystalline body in which the same loses its original colour and instead becomes blue-grey”.
However, Archigenes, who practised at Rome in the time of Trajan (98-117 AD), used the term “ophthalmosglaucos” for a curable blindness that was not caused by cataract.
Archigenes revealed that he used the juice of the deadly nightshade, a mydriatic, in the treatment of this condition, adding, “the instilled juice of nightshade makes black the grey eyes.”
Galen (129-216 AD), (Figure 3.) defined glaucoma as a condition in which changes in fluids of the eye caused the pupil to become grey. He also refers to the mydriatic effect of nightshade.
Aetius, the physician of the emperor Justinian (482-565) AD, and a great Ophthalmologist, identified two forms of glaucoma, one a curable condition of the lens and the other an incurable condition that involved an effusion in which the pupil becomes thickly coagulated and dried.
Second period (1600 TO 1854)
This period is marked by the rising awareness among ophthalmologists that technology is a key to a proper diagnostic.
Glaucoma became more distinct when it comes to adult or elderly patients with the emergence of four characteristics: (1) the consistent failure of cataract operations to improve vision, (2) the clinical appearance of eyes in terminal stages of the disease, (3) a specific history indicating self-limited forerunners of the severe disease, and (4) the elevated intraocular pressure.
Important breakthrough in ophthalmology is marked with the anatomic findings of Brisseau (1707) and the introduction of the process of lens extraction by Daviel (1752). This led to a search for the site of glaucoma in other structures of the eye and to concentration on clinical signs that could be helpful in distinguishing between cataract and glaucoma. Since a majority of the eyes in which the diagnosis of glaucoma was made in the 18th century were in an advanced stage of visual loss and iris atrophy after one or several acute attacks or after a prolonged chronic course, the clinical picture was dominated by congestion (varicosities) of the anterior ciliary veins, a dilated, poorly reacting pupil, and a varying degree of nuclear lens opacity. On examination with the light sources of that period, a greenish reflection could often be obtained; since this seemed to point to the real location of the disease, it became a prominent sign listed in the literature of the 181h and early 191h centuries.
The clinical features of advanced glaucoma, occasionally preceded by attacks of blurred vision that recurred with a high degree of uniformity, was first recorded in St. Yves\' “Treatise of the Diseases of the Eyes” (1741) and was described in more detail by Weller (1826).
It is a well known fact that elevation of the intraocular pressure as a distinct sign of ocular disease, recognizable by undue resistance of the eyeball to indentation by the physician\'s finger, was first clearly mentioned in the “Breviary” of the itinerant English oculist Banister (1626). In 1738 an equally clear reference to hardness of the eye appeared in the independent writings of Johann Platner, professor of anatomy, surgery, and therapeutics at the University of Leipzig. As a distinct clinical symptom, hardness of the eyeball was apparently generally known and accepted in the 1820s, as one may judge from the almost simultaneous but independent texts by Demours of France (1818), Guthrie of England (1823), and Weller of Germany (1826).
William Mackenzie had a great influence on European and American ophthalmology through his personal teaching and through his textbook, between 1830. and 1854. He distinguished between acute and chronic glaucoma and gave a detailed description of the course of the latter from a stage 1 characterized just by a greenish hue reflected from the pupil to a stage 6 in which the eyeball, after perforation of a corneal ulcer in absolute glaucoma, has become atrophic. Mackenzie was well aware of the abnormal hardness of the glaucomatous eye from the second stage on; also, he apparently was the first to recommend a form of posterior sclerotomy to relieve the abnormal hardness.
Duke-Elder in his System of Ophthalmology, also, in detail described this second period.
Third period (1854. to the present day)
With Eduard Jaeger, the grandson and son of distinguished Austrian ophthalmologist, began modern ophthalmology and modern ophthalmic exam. He was the first investigator who described and documented with the picture, ophthalmoscopic appearance of the glaucomatous disc in the literature. It was a picture from the monocular indirect ophthalmoscope, on which was described the glaucomatous disc as a swelling of the papillary tissues with respect to the surrounding retina.
Just a few months later, Albrecht von Graefe also described a prominence of the papilla in glaucoma. His description of the optic disc, specially his description of the pulsation of the arteries in the glaucomatous eyes, became reliable and, at time, reliable indicator of elevated intraocular pressure. The ring-shaped zone around the disc was officialy named - halo glaucomatosus. At the von Graefe’s clinic, after many examinations on rabbits with congenital fundus anomalies (i.e. coloboma of the uvea an optic nerve), examiners could not agree from ophthalmoscopic examinations whether observed parts of the fundus are elevated or depressed. The anatomic examination revealed tissue depression. This was confirmed by von Graefe’s assistant, Adolf Weber, who will later in his life made significant contributions to the understanding of the mechanisms of glaucoma. His analysis of the monocular indirect ophthalmoscopy revealed several factors, partly optic and partly perceptual, which caused misinterpretations of relative depth in the fundus.
Later, pathological findings confirmed ophthalmoscopic findings of the optic disc depression, what was interpreted as an effect of the elevated intraocular pressure, or- pressure excavation of the papilla. This had profound effect on von Graefe’s theory and made him examine all known symptoms of glaucoma and their link with elevated intraocular pressure. This research turned intraocular pressure from a simple symptom to an “essence” of glaucoma.
Early classification of glaucoma was made at von Graefe’s clinic.
First type of glaucoma was acute or inflammatory, which characterized with self-limited prodromal attacks of misty vision (in 70 % of the cases), patient is seeing rainbows around the candle flame; attacks increased in severity, length and frequency until the real disease suddenly erupted in the form of an acute attack of inflammation and severe reduction of vision. Partial vision recovery with temporary remission mostly occurred spontaneously or responding on a treatment with large doses of opiates, antiphlogisthics and paracenthesis. Many penetrating exams were carried out during the remissions. After analysis of all phases of this type of glaucoma, von Graefe made a concept according which an acute glaucoma is:”achoroiditis or an iridochoroiditis, with diffuse impregnation of vitreous and aqueous with exudative material which caused the rise in pressure through an increase in volume.”
Second type was the chronic glaucoma. Prodromal attacks were without any sign of irritation, congestion or swelling; lengthen gradually and fused in a chronic form, characterized with the anterior ciliary veins dilatation, shallow anterior chamber, iris atrophy, glaucomatous cupping, arterial pulsation in the fundus; followed with reduction in vision.
The third type von Graefe simply named amaurosis with excavation of the optic nerve, and for him it was not in a group of glaucomatous diseases. Normal anterior segment, with optic disc excavation, which lead to the vision impairment.
Completing this classification, von Graefe used the designation glaucomatous diseases for a disorders or conditions which secondarily lead to glaucoma and thereby may result in blindness.
In the late period of his research (1861.), von Graefe declared that an exclusion of amaurosis with the optic disc excavation from the group of glaucoma diseases was a mistake. This correction he credited to Doners of Utrecht, his friend, who found a palpable tension among many eyes with so-called amaurosis with optic nerve excavation to be significantly above normal. Doners, after his research, prepositioned a term glaucoma simplex, for the glaucoma without anterior segment manifestations and other complications, and glaucoma with ophthalmia, for those disorderswhere other manifestations appeared, especially in the anterior segment. The common cause of all glaucoma-the elevated intraocular pressure, Doners ascribed to a hyper secretion of intraocular fluid due to irritation of secretory nerves.
It is interesting that von Graefe discovered also an ocular hypertension patients among his amaurosis with optic nerve excavation cases. He accepted Doner’s term glaucoma simplex. His posterity, first of all Schnabel, had verb his amaurosis with optic nerve excavation, implying that it was an optic nerve disease unrelated to elevated intraocular pressure.
Theory of inflammation, that von Graefe’s proposed as a cause of intraocular pressure rise and a name of that type of glaucoma held until the clinical discovery of the angle closure mechanism in the 20th century. Some of the alternative terms that were used are: “irritative” (de Wecker), “congestive” (Hansen-Grut), and, much later, “uncompensated” (Elschnig).
The Anglo-Saxon literature preferred terms as acute, subacuteand chronic glaucoma
Finally, von Graefe in his last communication (1869), for the first time introduced a terms primary and secondary glaucoma.
In the late 1850s, German anatomist Heinrich Mueller was the first who granted ophthalmoscopically observed depression of the optic disc. In his theory that was a result of an abnormally increased vitreous pressure acting upon the lamina and forcing it to recede. Mueller and his followers assumed that the receding lamina had taken the entire papilla with it, placing the nerve fibres on a steadily increasing stretch or pressing them against the sharp edge of the excavation. Consequence of that was optic nerve atrophy.
Considering that this concept was not uniform for all glaucomatous eyes (in some cases pathologists confirmed the lamina cribrosa displacement, in others not), the theory was add to the basic pressure hypothesis and was widely accepted but also a new ophthalmoscopic and pathologic facts of glaucoma were revealed.
Austrian ophthalmologist Isidor Schnabel (1842-1908) was the first to describe in detail the nerve fibre breakdown with the formation of cavities as a characteristic of the glaucomatous process in the optic nerve. It was the earliest sign and for a long time the only glaucomatous change. In later stages the atrophy affected all portions of the optic nerve up to the entrance of the central vessels. In his opinion, cavernous atrophy was the glaucomatous atrophy. Schnabel saw the mechanism of the glaucomatous optic nerve disease in a process of imbibition of pathologic fluid from the vitreous by the nerve fibres, a process independent of the intraocular pressure. His findings were partly confirmed and partly refuted by subsequent investigators.
Another perspective on the origin and nature of the glaucomatous optic neuropathy was introduced by Priestley Smith. The glaucomatous cup is not a purely mechanical result of exalted pressure, but is in part at least, an atrophic condition which, though primarily due to pressure, includes vascular changes and impaired nutrition in the area of the disc and around its margin which require a considerable time for their full development.
This Priestley Smith\'s original notion that the rise in pressure may cause damage to the tissues of the disc through its influence on blood circulation is valid until the present day.
Previously mentioned essence of glaucoma, recognized in the mid-1850s, attributed to excessive formation of intraocular fluid or hyper secretion and assumed to eider a type of choroiditis (von Graefe) or a secretory neurosis (Donders).
The clear concept of the eye mechanisms that were involved in the intraocular pressure production, in that time, was not plain. German anatomist Schwalbe12 began in 1860s the experimental study of the fluid exchange of the eye, searching the lymphatics in the anterior segment. When the dye is injected into the anterior chamber of the eye, in aqueous solution or suspension, it appears promptly in veins on the surface of the globe! His conclusion was that the anterior chamber is a lymphatic space in open communication with anterior cilliary veins.
Theodor Leber13 also injected dyes into the anterior chamber of the eye of a rabbit, and discriminated certain border structures. This disclosure stimulated many investigators of that time, including Leber, to investigate a cannular system and Schwalbe, to investigate the anterior chamber angle in animals. Thus Leber discovered normal outflow (on a fresh enucleated mammalian eye), he presented it as a filtration through the trabecular meshwork and a flow through ciliary and vortex veins.His conclusion was that the rate of outflow was, in principle, proportional to the perfusion pressure, except during an initial period, when the perfusion fluid took up the space occupied in the living eye by blood. He actually determined filtration coefficients, the forerunners of today\'s coefficients of aqueous outflow.
Since this outflow was from fresh enucleated eyes at the pressures prevailing in the living eye, Leber reasoned that the same process of outflow must also take place in the normal living eye. To maintain a stable in vivo pressure, the steady loss of fluid must be compensated for by steady formation of an equal amount of fluid, which Leber believed could also take place through a process of filtration. Thus, the filtration theory of aqueous formation and elimination was born. In a few human eyes enucleated in far-advanced stages of glaucoma, Leber found very low filtration coefficients which indicated abnormal resistance to aqueous outflow. This finding fitted in well with the first detailed pathologic report on the condition of the chamber angle in far-advanced glaucoma: “The most important finding in genuine glaucoma is the circular adhesion of the iris periphery to the periphery of the cornea or the obliteration of the space of Fontana.”*
*Although Kieser of Göttingen had clearly shown in 1804 that the spaces described by Fontana in the eyes of herbivores did not exist in man, the term “Fontana\'s space” was still used in the 1870s and 1880s for the intertrabecular spaces of the human corneoscleral meshwork. Only the detailed studies of the region begun by Schwalbe in 1870 and continued by others made the term “Fontana\'s space” clearly inapplicable to the human eye.
Considering that in either case glaucoma could result from an inflammatory or an obstructive process within the angle or from pressure from behind. It was realized almost immediately that the peripheral anterior synechiae could be either the cause or the effect of glaucoma. Pathologic specimens which supported these mechanisms were identified and reported. The theory that glaucoma was principally a disorder of aqueous outflow (referred to generally as the Leber-Knies theory) rapidly gained ground.
The essence of the Leber’s (Leber-Knies) filtration theory has stood the test of time. Leber’s best apprentice, Erich Seidel, in 1920’s, made some necessary additions to this theory, including the effects of the colloidosmotic pressure of the plasma proteins and of active transfer processes in the formation of aqueous.
Interesting appendage is that the essence of the Leber’s theory, the idea, admittedly without experimental proof, of a steady directional circulation of fluid through the chambers of the eye had been expressed by earlier observers, specifically William Porterfield, more than 100 years before Leber.
During 1880s and 1890s, it was observed that chronic inflammatory glaucomas composed two thirds of all glaucomas. Angle closure glaucomas were dominant. Priestley Smith measured the horizontal corneal meridian in normal eyes 11.6mm and in glaucomatous eyes 11.2mm, what expressed dominance of the angle closure glaucoma in that period. 1888. Priestley Smith also introduced the concept of a predisposition to glaucoma, which consists in progressive narrowing of the circumlental space with age, due to the steady growth of the lens in eyes with small corneas. Anatomicaly, the ciliary processes in states of hyperaemia are crowded forward, pressing the iris against the anterior angle wall. This based on a Smith\'s experiment on the animal that a small excess of pressure in the vitreous chamber (as little as 4 mm Hg) makes the lens and the suspensory ligament advance in such a manner as to close the angle of the anterior chamber.
Next step was the discovery of shallowness of the anterior chamber as an important role in the mechanism of the acute glaucoma (in the eyes with acute inflammatory glaucoma). The description of the mechanism: if the pupil dilates in an eye with shallow anterior chamber, the iris, particularly with its thicker portion, can occlude the filtration angle and, thereby, raise the intraocular pressure. If contraction of the sphincter frees the filtration space, the event remains a prodromal attack. At a certain level of intraocular pressure the ocular veins are compressed at their place of entry into the sclera; venous stasis develops with increased transudation; that, and not inflammation, is the true nature of glaucoma.
The Revolution on this field came in 1920.when Curran (Kansas City) and Seidel (Heidelberg), on the basis of astute clinical observations, independently announced the concept of the relative pupillary block.
Curran\'s paper: "normally the aqueous passes through the pupil from the posterior to the anterior chamber, but it is here contended that in glaucoma this passage is impeded on account of the iris hugging the lens over too great a surface extent. Some of the aqueous gets through while some passes back, forcing the lens and the iris still more forward. "
Ophthalmoscopy, the most important single invention in ophthalmology, that had shaped its evolution, was introduced by Hermann von Helmholtz in December of 1850.,However, Jan Purkinje (known for the Purkinje images) had described the complete technique and published it in Latin in 1823,but his audience apparently was not yet ready and his publication went unnoticed. A quarter of a century later, however, the situation changed.
The ophthalmoscope was not based on any radically new concepts. Rather, it combined the appropriate application of various known principles with recognition of its potential impact and presentation to an appropriate audience. Under the leadership of men like Bowman in London, Donders in Holland, and von Graefe and von Helmholtz in Germany, ophthalmology emerged as the first organ-based specialty in medicine.
Bowman (1816 to 1892) is known for Bowman\'s membrane and for his work in anatomy and histology.\n\t\t\t
Donders (1818 to 1889) clarified the principles of refraction and accommodation (1864) and defined visual acuity as a measurable quantity. His coworker Snellen developed the Snellen chart.\n\t\t\t
In Berlin, Albrecht von Graefe (1828 to 1870) was a leader in stimulating the clinical application of new techniques and the careful documentation of new findings. He is remembered for Graefe\'s knife and Graefe\'s Archives (1854) (one of the first ophthalmic journals), and he founded the German Ophthalmological Society (Heidelberg, 1857).
Several workers had tried to visualize the inside of the eye but had fallen short of putting it all together. Kussmaul (known for “Kussmaul\'sairhunger”) described the imaging principles in a thesis in 1845but failed to solve the illumination problem. Cumming(1846) in England and Brücke(1847) in Germany had shown that a reflection from the fundus could be obtained by bringing the light source in line with the observer, but they failed to solve the imaging problem. Babbage,the English mathematician, reportedly constructed an ophthalmoscope in 1847, but his ophthalmologist friend did not recognize the importance and did not publish it until 1854, when von Helmholtz\' instrument was well known.
In the fall of 1850, von Helmholtz tried to demonstrate the inside of the eye to the students in his physiology class. On December 6, he presented his findings to the Berlin Physical Society; on December 17, he wrote to his father:
"I have made a discovery during my lectures on the Physiology of the Sense-organs, which was so obvious, requiring, moreover, no knowledge beyond the optics I learned at the Gymnasium, that it seems almost ludicrous that I and others should have been so slow as not to see it…. Till now a whole series of most important eye-diseases, known collectively as black cataract, has been terra incognita…. My discovery makes the minute investigation of the internal structures of the eye a possibility. I have announced this very precious egg of Columbus to the Physical Society at Berlin, as my property, and am now having an improved and more convenient instrument constructed to replace my pasteboard affair…"
Helmholtz\' monograph on ophthalmoscopy was published in 1851 and soon was widely circulated. The next year there were several important improvements contributed by other workers. Rekoss,von Helmholtz\' instrument maker, added two movable disks with lenses for easier focusing. Epkens, working with Donders in Holland, introduced a perforated mirror for increased illumination. Ruete in Germany did the same and also developed the indirect method of ophthalmoscopy. With these basic components in place, future generations provided technical improvements. In 1913, Landolt listed 200 different types of ophthalmoscopes.
If the patient\'s fundus is properly illuminated, the field of view is limited by the most oblique pencil of light that can still pass from the patient\'s pupil to the observer\'s pupil (Figure 4.). In direct ophthalmoscopy the retinal point that corresponds to this beam can be found by constructing an auxiliary ray through the nodal point of the eye. The point farthest from the centerline of view that can still be seen is determined by the angle α, that is, the angle between this oblique pencil and the common optical axis of the eyes.
Angle α, and therefore the field of view, is increased when the patient\'s or the observer\'s pupil is dilated or when the eyes are brought more closely together.
The more peripheral pencils of light use ever-smaller parts of each pupil. This means that, even if the patient\'s fundus is uniformly illuminated, the luminosity of the fundus image gradually decreases toward the periphery, so that there is no sharp limitation to the field of vision. In practice, therefore, the effective field of vision is determined by the illuminating system not by the viewing system. Most ophthalmoscopes project a beam of light of about one disc diameter.
Even with appropriate illumination, direct ophthalmoscopy has a small field of view (Figure 5.) shows that of four points in the fundus, points one and four cannot be seen because pencils of light emanating from these points diverge beyond the observer\'s pupil. To bring these pencils to the observer\'s pupil, their direction must be changed (Figure 6). This requires a fairly large lens somewhere between the patient\'s and the observer\'s eye. This principle was introduced by Ruetein 1852 and is called indirect ophthalmoscopy to differentiate it from the first method, in which the light traveled in a straight, direct path from the patient\'s eye to the observer.
The use of the intermediate lens has several important implications that make indirect ophthalmoscopy more complicated than direct ophthalmoscopy.
The primary purpose of the ophthalmoscopy lens is to bend pencils of light toward the observer\'s pupil. Figure 3 also demonstrates one of the most characteristic side effects of this arrangement: Compared with the image in direct ophthalmoscopy, the orientation of the image on the observer\'s retina is inverted. For the novice, this often causes confusion in localization and orientation. Figure 3 further shows that in this arrangement the patient\'s pupil is imaged in the pupillary plane of the observer. In optical terms the pupils are in conjugate planes.
The most important changes are related to the change from candle light to gas light, to external electric light and, finally, to built-in electric light sources.
Although the older generation found it difficult to adapt to the new instrument, the younger generation did so eagerly. One of them was Eduard von Jaeger (1828 to 1884) from Vienna, best known for his print samples that were based on the print catalogue of the Vienna State Printing House. He was the son of a well-known ophthalmologist and an artistically gifted mother. In 1855, at the age of 27, he published his first atlas; he continued to add to his collection of authoritative fundus paintings until his death in 1884.
Although not generally considered as a method of ophthalmoscopy, fundus examination with the slit lamp offers an important addition to the traditional methods of direct and indirect ophthalmoscopy. It offers the advantage of high-power magnification through the microscope and flexible illumination with the slit-lamp beam. With appropriate contact lenses, it can offer higher magnification than direct ophthalmoscopy and a field several times wider than indirect ophthalmoscopy. These methods have become particularly important in combination with laser treatment.
Because the slit-lamp microscope has a fixed focus on a plane approximately 10 cm in front of the objective and because the image of the fundus of an emmetropic eye appears at infinity, the fundus cannot be visualized without the help of additional lenses. There are several options.
A negative lens placed in front of the objective of the microscope can move the microscope focus to infinity. The practical application of this principle was worked out by Hruby,of Vienna (1942) with a lens known as the Hruby lens.
The optical principle is best understood if the lens is considered in conjunction with the eye, rather than as a part of the microscope. Parallel rays emerging from an emmetropic eye are made divergent by the Hruby lens and seem to arise from the posterior focal plane of that lens (Figure 7A.) For a -50-D lens, this would be 20 mm behind the lens (the usual Hruby lens is -55 D). The slit-lamp microscope is thus looking at a virtual image of the fundus in a plane somewhere in the anterior segment and must be moved a little closer to the patient than it would be for the regular external examination.
To estimate the field of view in this method, it may be assumed that only rays emerging parallel to the axis will reach the objective of the microscope and the observer\'s eye. When emerging from the eye, these rays must have been aimed at the anterior focal point of the Hruby lens. (Figure 7B), in which these rays are traced back to the retina, shows that the field of view (a) is proportional to the pupillary diameter as seen from the anterior focal point of the lens. This field is of the same order of magnitude as the field in direct ophthalmoscopy; it is largest when the lens is closest to the eye.
With the lens close to the cornea, the fundus image will be close to the fundus plane and approximately actual size. The magnification to the observer is thus largely determined by the magnification of the microscope. At 16×, the magnification is about equal to that of direct ophthalmoscopy; at higher settings, the magnification is greater. Binocular viewing and slit illumination are advantages over direct ophthalmoscopy, even at similar magnification. Limitation to the posterior pole is a disadvantage.
When the Hruby lens is moved progressively closer to the eye, it will eventually touch the cornea and become a contact lens. If the curvature of the posterior lens surface equals the curvature of the anterior corneal surface, the image formation will not change, but two reflecting surfaces will be eliminated, and image clarity will increase.
The use of a contact lens for fundus examination was perfected by Goldmannof Berne, Switzerland (1938). His contact lens is known for the three mirrors incorporated in it. These mirrors positioned at different angles make it possible to examine the peripheral retina with little manipulation of the patient\'s eye or of the microscope axis (Figure 8).
The refractive power of the cornea is eliminated in the contact lens. The only effective refractive element left would seem to be the far less powerful crystalline lens. The retina is situated well within the focal length of this lens, and the crystalline lens will therefore form a virtual image of the fundus (F) in a plane (F\') behind the globe. How can the microscope focus on an image that far back? We overlooked one other refracting surface: the plano front surface of the contact lens. F\' is seen through plastic and vitreous. To the observer in air F\' appears at F", through the same effect that makes a swimming pool appear shallower than it is. Because of this, the microscope again must focus on a plane inside the globe. As with the Hruby lens, magnification is largely determined by the microscope.
Thus, contact lens fundus microscopy extends our range of examination methods to details beyond the reach of ordinary direct ophthalmoscopy.
The use of the Hruby lens and Goldmann contact lens is comparable to direct ophthalmoscopy, because no real intermediate image is formed. The equivalent of indirect ophthalmoscopy can be achieved by focusing the microscope on the real image formed by a high-power plus lens.
El Bayadiintroduced the use of a +60-D lens for this purpose. The inverted image formed by this lens is situated 16 mm (0.0167 m) in front of it. A practical problem with some older slit lamps is that they cannot be pulled back far enough to observe this image.
Compared with the Hruby (-55 D) lens, the El Bayadi (+60 D) lens offers the same major advantage as does indirect ophthalmoscopy: a larger field of view. With proper placement of the lens, the field is about six disc diameters (40 degrees), compared with the one- or two-disc diameter field of the Hruby lens.
With a 60-D lens the aerial image is as large as the fundus; thus the magnification is approximately equal to the microscope magnification (similar to that with the Hruby lens).
Can the field of view be widened even further? This is possible by using a contact lens of very high plus power with some additional optical tricks.Figure 9 illustrates the RodenstockPanfunduscope, based on a design by Schiegel.
The unit contains a high plus contact lens, which forms an inverted fundus image (F\') located inside a second, spherical glass element.
In this arrangement, as in the previous example of a high myope (Figure 10), the image-forming and field-widening functions of the ophthalmoscopy lens are separated again. The contact lens forms the image; the spherical element serves to flatten the image and to redirect the diverging pencils of rays toward the observer. Because these elements are so close to the eye, the field of view can be very wide. Indeed, without moving the lens, the view reaches 200 degrees, that is, from equator to equator, 4 to 5 times the diameter (16 times the area) of regular indirect ophthalmoscopy or of the El Bayadi lens.
The size of the image inside the front lens is 70% of the retinal size; for detailed examination, therefore, 50% more microscope magnification is required than with the other slit-lamp methods. However, the principal use of this lens is not for its magnification but for its overview, an overview previously achievable only in fundus drawings or photocompositions.
Similar contact lens arrangements are used in specially designed fundus cameras that allow fundus photography of areas 100 degrees or more in diameter. With lenses such as these, the spectrum of our examining methods can be extended not only toward higher magnification than with direct ophthalmoscopy but also, at the other end, toward an overview of the fundus considerably beyond that obtainable with regular indirect ophthalmoscopy.
As the technology to calculate, design, and manufacture lenses with aspheric surfaces has improved, it has been possible to make lenses with higher powers and better light gathering abilities. The number and variety of lenses for indirect ophthalmoscopy and of contact lenses for slit-lamp microscopy has grown accordingly.
Fundus cameras have greatly improved the ability to document and follow fundus lesions. Eduard von Jaeger often spent countless hours drawing a single fundus, but today a photographic image is available in a fraction of a second. For reasons mentioned earlier, fundus cameras are built on the principle of indirect ophthalmoscopy. The observer\'s lens and retina are replaced by a camera lens and film. Because all components are enclosed in a rigid housing, more accessories can be built in. This includes a dual illumination system, which includes a constant light source for focusing and a flash for photography, and filters such as for fluorescein angiography. Rather than placing the viewing and illumination beams side by side, the illumination beam generally uses the periphery of the pupil and leaves the center for the observation beam.
An angled glass plate that can be flipped to the right or to the left can be used to slightly deviate the observation beam to the right part or the left part of the patient\'s pupil to produce photo pairs that can be viewed stereoscopically.
Because newer lens designs have allowed the construction of wide-angle cameras, a special challenge has been to construct the optical system in such a way that the curved retina is imaged in a plane that can be captured on a flat film.
The optics of the eye are not perfect. Even if major errors are corrected with spherical and cylindrical lenses, small irregularities across the pupillary opening persist. The technique of adaptive optics was developed for astronomical telescopes to counteract image degradation by atmospheric irregularities. An adaptive optics system uses a grid to divide the pupillary opening into many small areas and determines a separate small correction for each area. The information is fed to a slightly deformable mirror with microactuators. Thus the image quality can be enhanced to the point at which the cone mosaic can be clearly visible. The setup is too laborious for use in routine photography. Because the corrective system has to be fixed in relation to the pupil, it cannot be implemented in glasses or contact lenses. However, the technique, also known as wavefront analysis, has found a place in the refractive sculpting of the cornea.
Another important part of ophthalmic exam. First explored in by Trantas (1907.); then explored by Salzmann (1915-16.); Koeppe (1919-20.); and Troncoso (1925-30). Finally Otto Barkan (1887.-1958.) made gonioscopy a routine diagnostic method in the ophthalmologist\'s office, thereby bringing about the separation of the glaucomas due to the angle-closure mechanism from the open-angle glaucomasthat the elevation of the intraocular pressure depends of abnormal resistance to aqueous outflow caused by anatomic or functional changes within the outflow channels.
Not until the 1890s did open-angle glaucoma become well proved and accepted in theories.
Thanks to gonioscopy, started recognition of a type or types of glaucoma without obstruction of the angle by the iris.
In the first edition of the Graefe-SaemischHandbook of Ophthalmology (1877), Saemisch lists the following ocular diseases as frequently giving rise to secondary glaucoma: cicatricial ecstasies of the cornea, circular or total adhesions of the iris to the lens, iritis serosa, traumatic cataract, dislocations of the lens, intraocular tumours, hemorrhagic retinal processes (referring mainly, if not exclusively, to occlusions of the central retinal vein), and sclerectasia pastries (which probably referred to glaucoma in eyes with malignant myopia). Congenital hydrophthalmos was at the time also classified with the secondary glaucomas.
William Bowman introduced digital estimation of the ocular tension at the annual meeting of the British Medical Association in 1862. Estimation of the ocular tension by palpation became one of the ophthalmologist\'s special skills, and some ophthalmologists developed so much confidence in it that they viewed instrumental tonometry with suspicion.
The early beginning of instrumental tonometry, apparently made by von Graefe, who mentions preliminary trials of mechanical tonometers in a letter to Donders dated December 24, 1862. Unfortunately, none of these instruments, however, reached the drawing board stage.
The real beginning and the first tonometers actually produced and tested on human eyes were developed in Donders\' clinic in Utrecht between 1863 and 1868. They were instruments for use on the sclera. The scleral curvature at the site of tonometer application was determined first; it then served as a reference plane for the measurement of the depth of the indentation.
Impression tonometry had its drawbacks. The principal flaw was that the indentation, by displacing a significant amount of intraocular fluid, changes the pressure which is intended to measure; this was clearly expressed for the first time by AdoIf Weber in 1867. Weber was official inventor of the first applanation tonometer, which was intended to give a tension reading with only minimal fluid displacement. Despite its theoretic superiority, this instrument did not gain wide acceptance, because recognition of the point of perfect applanation without indentation proved to be difficult. Lately, the principles of applanation tonometry were explored by Maklakoff in 1885. andImbert and Fick, father and son, a few years later. It resulted a several new applanationtonometers, but only one of them, Maklakoff\'s model of 1892, has stood the test of time and has remained in use, mainly by groups in the USSR.
The beginning of the 20th century, digital tonometry was still a method of subjective assessment of the ocular pressure . At that time neither applanation tonometer did not find widespread use in practice.Finally, in 1905.Schiøtz presented his impression tonometer and it did not take long for the instrument to acquire the epithet “the first clinically useful tonometer.” First major comprehensive reports of the clinical value of Schiotz tonometer began to appear in 1910. The essence of today\'s knowledge of the intraocular pressure in the normal and in the diseased human eye was acquired between 1910. and 1920. through the use of Schiøtztonometers.
Disadvantages of digital and instrumental tonometry, realized by the pioneers of these methods, addressed to the properties of the eyeball wall, especially elasticity, affected estimation of the intraocular pressure. Early experimental attempts in that time, to measure these properties and to eliminate them revealed new variables. Schiøtz wrote in 1920: “I can not imagine any method available for living eyes by which errors due to variations of the envelope could be eliminated.” Thirty years later, the electronic form of his instrument came closest to yielding reasonable estimates of “ocular rigidity,” the term introduced by Friedenwald for the resistance that the in vivo eyeball offers to a change in intraocular volume .
Correcting readings taken with the Schiøtz tonometer for deviation of the particular eye from average ocular rigidity, the coefficient of ocular rigidity lost some of its clinical importance through the tremendous progress in applanation tonometry that occurred in the early 1950s through the work of Goldmann, Perkins, and Maurice.
The technology to estimate intraocular pressure (IOP) has evolved tremendously since Sir William Bowman emphasized the importance of ocular tension measurements in 1826. In an address delivered at the annual meeting of the British Medical Association, Sir William underscored the critical role that digital estimation of ocular tension played in his practice. In his address, Sir William stated that “it is now my constant practice, where defective vision is complained of, to ascertain almost at the first instant the state of tension in the eye...It is easy enough to estimate the tension of the eye, though there is a right and a wrong way of doing even so simple a thing... With medical men, the touch is already an educated sense, and a very little practice should suffice to apply it successfully to the eye.”
Soon afterwards, digital tonometry became an essential clinical skill necessary to master by all ophthalmologists. When mechanical tonometry was first introduced in the late 1800s, many ophthalmologists felt so confident with their ability to estimate IOP by palpation that they viewed the new technology as inferior. Isador Schnabel, in an address to the Vienna Ophthalmological Society in 1908, was noted to state that although he did not object in principle to mechanical tonometry, he expected “…very little from this test since digital tonometry by an expert is a much more accurate test”.
Although Grafe is credited with the first attempts to create instruments that mechanically measured IOP in the early 1860s, his proposed instruments were neither designed nor built. Rather, it was Donders who designed the first instrument capable of estimating IOP – albeit not accurately – with mechanical tonometry in the mid 1860s. The principle behind Donders’s instrument was to displace intraocular fluid by contact with the sclera. The ophthalmologist first measured the curvature of the sclera at the site of contact, and then used this measurement as a reference plane to measure the depth of indentation. Smith and Lazerat refined this technology in the 1880s, and the discovery of cocaine by Carl Koller in 1884 led the way to corneal impression tonometry soon thereafter. With the aid of a powerful corneal anesthetic agent, corneal tonometry became the definitive choice of IOP measurements because it offered a well – defined and uniform site of impression when compared with the sclera.
Impression tonometry’s major shortcoming was that it displaced so much fluid upon contact with the eye that the measured readings were highly variable and mostly inaccurate. What was needed was a way to displace a minimal amount of fluid to record IOP. This breakthrough came when Adolf Weber designed the first applanation tonometer in 1867, which gave a highly defined applanation point without indentation. After two decades of skepticism, the value of applanation tonometry was re-discovered when Alexei Maklakoff and others introduced new versions of applanationtonometers. In early 20th century, there were about 15 models of tonometers in use. In fact, Maklakoff’s 1892 model is the basis of applanation tonometry today. However, digital tonometry still remained the gold standard among most ophthalmologists in the early 1900s.
The first clinically useful mechanical tonometer was designed and introduced by HjalmarSchiotz in the early 1900s. The instrument was simple, easy to use, and highly precise. It was quickly accepted and became the new gold standard beginning the 1910s. Innovations in calibration led to its increased use, and a tremendous amount of knowledge about the normal and glaucomatous eye was quickly acquired. An adjustment for ocular rigidity was introduced by Goldmann in the 1950s, which led to the development of Goldmannapplanationtonometers. The Goldmanntonometers displace such little fluid that variations in ocular rigidity are mostly negligible. The electronic and non – contact tonometers used today rely heavily on the principles and instrumentation first introduced by Maklakoff, Schiotz and Goldmann.
Today, for the most part, digital tonometry has been replaced by sophisticated technologies to estimate IOP. Today’s instruments are incredibly accurate and easy to use. Yet, there is sometimes no good substitute for digital tonometry. For example, some ophthalmologists may prefer digital tonometry when estimating IOP in patients with keratoprostheses. In these situations, fingers that have mastered Sir William’s art are highly desirable. In fact, it is said that the famous Dr. Claus Dohlman, Harvard professor of Ophthalmology at the Massachusetts Eye and Ear Infirmary, remains as accurate in measuring IOP with his fingers as any ophthalmologist using the high-tech tonometers of today!
Modern diagnostic of glaucoma is unimaginable without perimetry. The merit for measurements of peripheral vision for the diagnosis and follow-up of ocular disease, as many other things in ophthalmology, is attributed to Albert von Graefe. With a primitive campimeter—a sheet of paper with radial rows of dots which served as stimuli—he was probably the first (1856) to plot paracentral field defects in chronic glaucoma and to use them in the evaluation of surgical results. Similar to von Graefe’s device, Haffmanns from Donder’s clinic discovered the greater frequency in glaucoma simplex of serious involvement of the upper half of the field, which gave rise to an easily detectable nasal step .
In 1857.Förster introduced the first perimeter, which placed accent on large targets, such as the 10/330, which permitted only very gross measurements. The observations of that time did suggest partial reversibility of field defects if the pressure was lowered substantially by an iridectomy or sclerotomy. 1889. was a very important year for a development of techniques most appropriate for glaucoma. Bjerrum presented 2-meter screen, the 2-meter test distance, and the 2- to 5-ram white test objects. He discovered the relative or absolute scotomas, circling the point of fixation and including the blind spot, which became the hallmark of chronic glaucoma. Conceptually, it means the beginning of the nerve fibre bundle theory of the glaucomatous optic nerve disease.
Further major step was the occurrence of small scotomas in the zone from 12° to 20° from the point of fixation, in early glaucomas, presented by Peter . These scotomas, in the beginning were not connected with the blind spot, but they reached it later via expansion.
The construction of smaller isopters, another early glaucoma characteristic, presented in 1920s, was clearly established with Bjerrum’s technique. Bjerrum’s technique also confirmed the regression of early glaucomatous defects following normalization of pressure documented by instrumental tonometry. The close relationship between pressure and field of vision was demonstrated further by Samojloff\'s observations of temporary enlargement of the blind spot concurrent with osmotically induced pressure elevations. By stereocampimetry with minute targets, Evans was able to detect a gross form of parallelism between diurnal pressure fluctuations and the size of paracentralscotomas.
Also in 1920s was noticed that among patients with glaucomatous defects close to the point of fixation (late stages of glaucoma optic neuropathy), a surgical procedure, particularly iridectomy, could have an untoward effect and lead to further rapid shrinkage of the visual field. The incrimination of the iridectomy referred originally to the period when the alternative, the sclerotomy, had proved relatively free of unfavourable effects on the visual field. Subsequent experience with filtering operations temporarily led to the distinction between two classes of glaucoma operations: 1) the less risky: cyclodialysis and sclerotomy and 2) the riskier: iridectomy, sclerectomy, and trephination.
Main discoveries where:
A curative action of the iridectomy in certain glaucomas7,,
The development of the filtering operations , and
The discovery of the first three ocular hypotensive drugs: eserine, pilocarpine, and epinephrine .
|\n\t\t\t\tSurgical Treatment of Glaucoma ( 1830-1920 )\n\t\t\t\n\t\t|
|1830\n\t\t\t||Mackenzie1 recommends scleral punctures to release vitreous and to relieve the pressure on the retina.\n\t\t|
|1857\n\t\t\t||von Graefe\'s iridectomy6 almost overnight gains the position of the glaucoma operation.\n\t\t|
|1882\n\t\t\t||de Wecker, in a paper on the “filtering cicatrix”9, expresses the concept that in the presence of elevated intraocular pressure, a properly executed corneoscleral incision can heal in a manner allowing intraocular fluid to “filter,” ie, be driven by a pressure gradient through the loose scar tissue into subconjunctival spaces.\n\t\t|
|1891\n\t\t\t||Bader  finds the occurrence of an iris prolapse during or shortly after an iridectomy a favourable sign, auguring success of the operation.\n\t\t|
|1903\n\t\t\t||Herbert reports on a series of subconjunctival fistula operations in which he purposely leaves the iris in the operative incision. The report includes the first detailed description of the transformation of the epibulbar tissues that become exposed to the steady flow of aqueous .\n\t\t|
|1905\n\t\t\t||Heine first reports on the operation of cyclodialysis, based on Fuchs\'  and Axenfeld\'s observation of the association between postoperative choroidal detachment, a tear or tears in the insertion of the ciliary muscle at the scleral spur, and hypotony.\n\t\t|
|1906\n\t\t\t||Lagrange first reports on his iridosclerectomy.\n\t\t|
|1909\n\t\t\t||Freeland and Elliot independently substitute the trephine for Lagrange\'s scissors.\n\t\t|
|1913\n\t\t\t||At the first international review of glaucoma surgery the pronouncement is made that chronic glaucoma can only be arrested by establishing a filtering cicatrix in connection with the anterior chamber. The iridectomy loses its status of the glaucoma operation but still is first in favor for acute glaucoma .\n\t\t|
|1915\n\t\t\t||The abexterno incision is introduced by Foroni.\n\t\t|
|1920\n\t\t\t||Seidel demonstrates the transconjunctival passage of aqueous after trephining procedures.\n\t\t|
|\n\t\t\t\tMedical Treatment of Glaucoma ( 1863-1932 )\n\t\t\t\n\t\t|
|1863\n\t\t\t||Argyll Robertson and von Graefe study the effect of extracts of the calabar bean on pupil and accommodation. Von Graefe finds the miotic effect useful in that it facilitates the iridectomy.\n\t\t|
|1876\n\t\t\t||Laqueur reports “a definite drop of the elevated tension after repeated installations of physostigmine in five cases of glaucoma simplex and in one case of secondary glaucoma.”\n\t\t|
|1876\n\t\t\t||Weber studies the mechanisms underlying the hypotensive effect of physostigmine in rabbits and in man and advises caution in its use because of the marked swelling and engorgement of the ciliary processes caused by the drug .\n\t\t|
|1877\n\t\t\t||Laqueur gives the first clear-cut account of the successful termination by use of physostigmine of attacks of acute glaucoma and of the prevention of recurrences .\n\t\t|
|1877\n\t\t\t||Weber introduces pilocarpine with the hope that it will replace the iridectomy in some of the chronic and simple glaucomas and that it will serve to make up for the insufficient effect of the latter in many other cases .\n\t\t|
|1898\n\t\t\t||The hypotensive effect of topically administered adrenal extracts is discovered.\n\t\t|
|1902\n\t\t\t||Darier reports significant lowering of pressure in some glaucomas, induced by adrenaline alone or in combination with physostigmine.\n\t\t|
|1909\n\t\t\t||Extensive clinical use of adrenaline has confirmed the beneficial results, but it has also brought to light the clear-cut untoward effects, ie, the drug may cause further elevation of pressure and even precipitate acute attacks in certain eyes.\n\t\t|
|1923\n\t\t\t||Hamburger reintroduces adrenaline; new, more potent, more stable preparations for topical use are becoming available. Untoward effects in certain eyes are rediscovered .\n\t\t|
|1932\n\t\t\t||Gonioscopy furnishes the answer to the unfavorable response of certain eyes to topical adrenaline.\n\t\t|
The United States health-care system has been moving to patient-centered care by incorporating different levels of collaborations, including those occurring within a health-care organization (HCO) or between HCOs [1, 2]. A classic model  proposed to understand patient-centered care divides the health-care system into four nested levels: (1) the individual patient; (2) the care team made up of health-care workers (e.g., clinicians, pharmacists, social workers, and utilization managers) to care for patients; (3) the HCO (e.g., hospital, clinic, and nursing home) that supports the development and work of care teams by providing infrastructure and complementary resources; and (4) the political and economic environment (e.g., regulatory, financial, payment regimes, and markets) that support hospital collaborations with other HCOs and payers on population health management. To promote patient-centered care, HCOs create infrastructures and develop staffing strategies to encourage collaboration among health-care workers to care for patients [4, 5]. Collaboration among health-care workers can improve care quality (e.g., reducing readmission rates) , patient safety (e.g., preventing medical errors) , and patient outcome (shortening length of stay) [8, 9, 10].
Staffing plans describe collaboration at a macro-level. For instance, an intensive care unit (ICU) may use an intensivist-centered care team (closed model) or an ad hoc group consisting of nurses, nurse practitioners, and physicians (open model) to care for critically ill patients . The macro-level staffing strategies seldom specify how health-care workers connect and how they receive and disseminate information to care for patients. Thus, it is difficult for HCOs to monitor those top-down staffing strategies implemented in clinical practice. Without the micro-level knowledge of teamwork (e.g., health-care worker connection), it is challenging for HCOs to assess their staffing strategies to identify inefficient and ineffective parts for further collaboration optimization.
Measuring connections among health-care workers is very challenging due to complex clinical workflows and dynamic structures of teamwork [12, 13]. That is also one of the reasons why HCOs do not specify connections among health-care workers in their staffing plans. Recent studies show connections among health-care workers can be learned from their activities in electronic health record (EHR) systems [14, 15, 16, 17, 18, 19]. EHR systems are a platform used by health-care workers to diagnose patients and exchange diagnostic results [20, 21]. In modern health-care environments, an increasing number of health-care workers utilize EHR systems as the primary tool to diagnose patients and exchange health information . Therefore, the volume and scale of the EHR system utilization data have been increasing exponentially in recent years, which provide abundant resources for researchers to learn collaborations through the EHR system utilization [14, 15, 16, 17, 18, 19].
In this chapter, we provide a network analysis of the EHR system utilization data to learn teamwork structures and specify connections among health-care workers. We believe the chapter can provide researchers a new way to model teamwork/collaboration in health care. We anticipate the data, methods, and applications introduced in this chapter will be of interest to the teamwork in health-care readership, particularly those focused on network analysis, secondary data analysis, EHR utilization, and care teams.
EHR systems provide a platform for care coordination across a diverse collection of health-care workers [22, 23, 24, 25]. Coordination activities occurring in EHR systems play an increasingly important role in the establishment of high-efficient health-care worker collaboration networks. Various studies, including our prior research, have leveraged health-care worker activities in EHR systems to infer patterns of collaboration [9, 10, 14, 15, 16, 17, 18, 19]. The proportion of care activities performed via EHR systems has steadily increased with the adoption of EHR through meaningful use of incentives [22, 26].
Health-care worker activities occurring in EHR systems have been documented in the form of audit logs. When a provider accesses or moves between modules in the EHR interface, such as moving from Progress Notes to Order Entry, a record of these activities are documented, including the time the event occurred, the health-care worker and patient IDs, and the computer location. Audit logs include all health-care worker interactions to EHRs of patients, which provides an opportunity to study connections among health-care workers. The continuous data collection of the EHR audit logs provides robust, readily available data. Since health-care worker activity is documented in the EHR in near real time, it is free from recall bias and variation introduced when health-care workers are retrospectively surveyed to describe their activities in EHRs.
The activities performed by health-care workers stem from six primary sources , including conditions (e.g., assigning a diagnosis), procedures (e.g., intubation), medications (e.g., prescription), notes (e.g., progress note writing), orders (e.g., laboratory test ordering), and measurements (e.g., measuring respiratory rate).
Figure 1 shows an example to illustrate health-care worker activities in EHR systems. Each event, such as requesting a lab test, includes a health-care worker, an EHR, and the time stamp. The four events depicted in the example demonstrate the hidden collaborations between health-care workers. For instance, the physician ordered a lab test and shared the order with the lab user; next, the lab user conducted the laboratory test and shared the test results of the patient with a health-care worker in the physician office; finally, the nurse practitioner reviewed and analyzed the results.
Events document interactions of health-care workers to EHRs of patients, but they do not capture the direct connections among health-care workers. As shown in Figure 1, the four health-care workers performed events to EHRs of a patient, and they are not directly connected. We leverage events to measure the hidden connections among health-care workers. A hidden connection between two health-care workers is defined based on their interactions with the EHRs of patients. We call a hidden connection as an indirect relationship, because the two health-care workers do not communicate directly, but care for the same patients via performing actions to their EHRs. For instance, a physician ordered a lab test and sent the order to a laboratory test user. The physician and the lab user have a hidden relationship that is built upon the lab test order. Hidden relations are essential knowledge to characterize processes of health information sharing and dissemination among health-care workers in EHR systems, which can potentially impact teamwork, and the following care quality and patient safety.
We use a bipartite graph of EHR users (health-care workers) and EHRs of subjects (patients) to represent events a user performed to EHRs of a subject. Figure 2 shows an example of a bipartite graph, and a binary matrix to characterize interactions of six users to EHRs of seven subjects. In the example depicted in the figure, we use a binary matrix to represent if a health-care worker performed events to EHRs of a subject within a period (e.g., hour, day, week, or length of stay). Researchers can determine the period and whether using a binary value or the number of events to represent interactions of a health-care worker with EHRs of a patient according to their research purpose. To simplify our process, we use a binary matrix A, as shown in Figure 2. As mentioned above, if two health-care workers performed events to EHRs of the same patients within a period (e.g., a day), then there exists a hidden relationship between them. For instance, u1 and u3 both performed events to EHRs of s1 and s2. Thus, in the binary matrix, A(1,1), A(1,3), A(2,1), A(2,3) are all ones. To transforming health-care works’ interactions to EHRs to connections among health-care workers, we use binary matrix multiplication. For instance, the relationship between u1 and u3 can be learned by multiplying matrix A and its transpose matrix AT. The results of matrix multiplication are shown in matrix B in Figure 3. B(1,3) or B(3,1) represents the number of subjects whose EHRs were managed by both u1 and u3. From Figure 2, we can see the number of subjects co-managed by both u1 and u3 is 2, which is equal to B(1,3) or B(3,1). The larger the cell values in matrix B, the more strength of the relationship between health-care workers.
Matrix A represents the interactions of health-care workers to EHRs of subjects, and B describes the relationships between health-care workers. We show a simple way (matrix multiplication) to learn B from A. There are many alternative or advanced approaches that can be applied to matrix A (binary or nonbinary version) to measure hidden relationships between health-care workers. Examples of such methods include term-frequency, inverse documentary frequency (TF-IDF) , principal component analysis (PCA) , and similarity measurements (e.g., cosine, Kullback-Leibler (KL) divergence, edit distance, and Jaccard distance) . For instance, if the size of the matrix is big (a large number of subjects or health-care workers), we can apply PCA to it to reduce the dimensionality first, and then measure relationships for pairs of health-care workers based on the principal components.
There are two types of relationships: directed and undirected between health-care workers. Directed relation emphasizes on the ordered relations, for instance, the connections from health-care worker A to B, and B to A that are different. To learn directed connection from the utilization data, we use time stamps of events to describe the ordered relationships. As shown in Figure 1, lab test ordering occurred ahead of lab test results uploading. Thus, the relationship between the physician who ordered the lab test and the lab user who uploaded the test results is directed. Upon the directed relations, we can create direct networks. We will use an example to illustrate the creation of directed networks of health-care workers concerning the management of each patient. Examples of undirected networks are used to describe structures of collaborations among health-care workers within a unit or across a HCO.
As mentioned above, we define actions performed by a health-care worker in EHR systems as events. Events affiliated with EHRs of a patient constitute a sequence of information flow. We provide a simple scenario to understand the series of information flow as follows.
“The night respiratory therapist documents an increased need for oxygen in a patient’s EHRs” → “the daytime nurse documents the patient’s vital signs and notes that the patient has tachypnea” → “on rounds the nurse practitioner and attending review the recorded vital signs focusing on the need for more oxygen and elevated respiratory rate” → “the physician prescribes a diuretic.”
In this example, the nurse practitioner and attending’s comprehension of the patient’s condition grew with each update to the EHR. Health-care workers depend on their colleagues to provide information for clinical updates as they are essential to health-care workers’ decision-making. As mentioned above, we call this virtual worker-worker interaction a hidden connection. A hidden connection does not mean a face-to-face interaction occurred, but rather, there existed the potential for the neighboring health-care workers to directly exchange information on the patient’s condition via the EHR and arrive at the same conclusion, which in this scenario was to prescribe medication for pulmonary edema. We build networks that represent the hidden connections facilitating the dispersion of patient-related information. We call them patient-level health-care worker networks because they are composed of all health-care workers that treated a common patient.
To start, we create a simplified sequence dataset by condensing consecutive events by the same health-care worker into a single event. In this scenario, we can filter the self-loop relationships of health-care workers. In a network or a graph, a self-loop relationship is an edge that connects a vertex/node to itself. For example, health-care worker W1 made three EHR events consecutively to EHRs of a patient, and we condensed them into one event; one could interpret the simplified sequence as a workflow in EHR. Based on the sequences, we identified relationships between health-care workers whenever their events occurred consecutively (health-care worker W2 used the patient’s EHR after health-care worker W1). We characterized each hidden connection with the frequency by which they occurred.
Figure 4 shows an example of how we build a health-care worker network from a patient’s sequence. As shown in Figure 4, the health-care worker W1 interacted with the EHR before health-care worker W2, so the arrowhead on the right points to health-care worker W2. The edge weight is the number of times the hidden interaction occurred. Note an edge exists if an interaction occurred at least once. While an observed interaction was not guaranteed to be an exchange of information, it did have the potential to be one.
The structure of teamwork learned from a single patient is hard to represent the pattern of collaboration concerning the management of a group of patients. In this section, we introduce the creation of undirected health-care workers for the management of a group of patients. We assume health-care workers participating in the care of the same patients (performed events to EHRs of the same patients) on the same day have a relationship. Based on such an assumption, we can create a binary matrix (as shown in Figure 3) to describe whether a health-care worker performed events to EHRs of a patient. The cell value 1 is for Yes, and 0 for No. Based on the binary matrix of health-care workers and EHRs of patients, we can use the matrix multiplication, as shown in Figure 3, to get the daily relationships between pairs of health-care workers. Each non-diagonal cell value shows the number of patients; any two health-care workers both performed events to their EHRs on the same day. Two factors determine the strength of the relationship between two health-care workers. The first is the number of patients the two workers performed events to their EHRs on the same day, and the second is the number of days when the two workers performed events to EHRs of the same patients. We build a health-care worker network for a group of patients by using the relationships which are cumulatively added based on the number of days and patients.
We use a simple scenario to explain health-care worker networks built upon a group of patients. Assuming a medical intensive care unit (MICU) adopted a new scheduling strategy in a pandemic (e.g., COVID-19), and the health-care organization plans to investigate the changes in the structure of collaboration among health-care workers before and after the adoption of the new scheduling strategy. In this scenario, we use 8 months (4 months before and after adopting the new scheduling strategy) of EHR utilization data to learn the changes. To implement the study, we create two groups: critically ill patients admitted to the MICU before and after the new scheduling strategy adopted. To ensure the studied two groups share similar confounding factors (e.g., demographics and health conditions), we can use propensity score matching to create them. We use events performed by health-care workers to EHRs of the two groups of patients to measure relationships between health-care workers before and after the adoption of the scheduling strategy, respectively. Based on the relationships, we can build two health-care worker networks: before and after the adoption of the new scheduling strategy. The differences in the structures of the two networks can be measured using sociometric measurements, which are introduced in the following sections.
When learning a collaboration network at the level of a health-care organization, the number of patients and health-care workers investigated will be much bigger, and the relationships between health-care workers will become more complex. If we have a large number of patients, then it may complicate the measuring of the relationships between health-care workers. For instance, if we investigate 10,000 health-care workers and 1,000,000 patients, then the size of the health-care worker-patient matrix is 10 K by 1 M. There is a necessity to reduce the dimensionalities of the matrix to ensure it is appropriate for the following approaches to measure relationships between health-care workers. As mentioned above, PCA can be applied to the matrix to reduce dimensionalities. After the dimensionality reduction, we can use similarity measurements (e.g., cosine similarity or KL divergence) to calculate the relationships between health-care workers, which are used to build networks of health-care workers. If PCA is unable to represent the variance of the data in the matrix, an alternative way is to transform the matrix of health-care workers and patients into a higher level. Instead of building networks of health-care workers, we can create networks of operational areas (e.g., medical intensive care unit, and burn center). Also, we can cluster patients into groups according to their phenotypes and transform the matrix of health-care workers by patients into operational areas by patient groups. Based on the new transformed matrix, we measure relationships between operational areas and build a collaboration network of operational areas.
Figure 5 shows an example to illustrate the process of transforming interactions between health-care workers and EHRs of patients into interactions of health-care workers to EHRs of groups of patients. Patient groups can be learned by conducting phenotyping algorithms on patient health conditions and demographics. For instance, a typical topic modeling algorithm – Latent Dirichlet Allocation (LDA) can be used to learn topics to represent phenotypes of each patient. Based on the phenotypic topics, patients can be clustered into groups. As shown in Figure 5, the transformation from Apatient × health condition to Bpatient × patient group can be implemented by using LDA.
Figure 6 shows an example to illustrate the process of transforming interactions between health-care workers and EHRs of patient groups into the interactions between operational areas and EHRs of patient groups, which are further leveraged to measure relationships between operational areas. The transformation from Dhealth-care worker × patient group and Eoperational area × health-care worker into Foperational area × patient group is implemented using matrix multiplication. Eoperational area × health-care worker represents the affiliations of health-care workers to operational areas. Similarity measurements can be applied to Foperational area × patient group to learn relationships between pairs of operational areas Roperational area × operational area. Collaboration networks of operational areas can be built upon the Roperational area × operational area. To learn stable relationships between operational areas, we may need to create the matrix Chealth-care worker × patient by setting a longer window size, such as 1 week/month rather 1 day we used in the previous examples. A study shows it requires at least 4 weeks to get stable relationships between operational areas by using interactions between health-care workers and EHRs of patients .
Concerns over the trustworthiness of the results of automated learning methods are not limited to the health-care worker network learned in this chapter. Instead, this is a problem that manifests when any knowledge is learned from the secondary analysis of EHR data. Researchers always need to review the knowledge learned from the data for their plausibility. As we mentioned above, the relationships between health-care workers learned from the utilization data are indirect. In other words, they are not explicitly documented by health-care organizations. To use networks built upon such relationships to describe or interpret structures of collaborations among health-care workers, we need to validate the relationships.
To do so, we design and deploy an online survey to assess the plausibility of relationships among health-care workers. Figure 7 depicts an example of 626 relationships ranked on a log scale and the strength of the relationships. This is clearly more relationships than a human can evaluate without fatigue, and so we need to sample a small number of them for respondents to assess. For instance, we can randomly select 20 relationships: 10 of high, and 10 of low strength. A survey can be designed to evaluate a specific hypothesis of the form: hospital employees can correctly distinguish between relationships of high and low strengths.
A survey contains a series of questions. The hospital employees who respond to the survey are presented with questions of the form: “An internal medicine physician performed actions to the record of patient John Doe. How likely is it that an internal medicine nurse practitioner performed actions to the same patient’s record?”. Respondents are not presented with the strength of the relationship between internal medicine physicians and nurse practitioners. The respondents are asked to choose one of five candidate answers: “Not at all likely,” “Slightly likely,” “Moderately likely,” “Very likely,” and “Completely likely.” In order to conduct a survey analysis through statistical models, we can convert these answers into integer values in the range 1–5 (e.g., “Not at all likely” is mapped to 1).
A set of respondents will answer each question in the survey. We can conduct a pretest to obtain feedback from the experts to refine the surveys and estimate the required number of experts via a power analysis. REDCap, which is a secure web application for building and managing online surveys and databases , can be used to implement the online survey. Details of the plausibility validation of the relationships between health-care workers can be found in our previous works [30, 32]. If we can verify with statistical significance that the learned relationships are often in line with the expectations of hospital employees, then we can suggest that collaboration networks of health-care workers, as well as strategies built on such networks, may be reliable and scalable.
Sociometric measurements include network- and node-level metrics. The network-level metrics such as size, graph density, reciprocity, triads, average path length, clustering, cohesion and density, core-periphery, centralization, diameter, and K-core are used to characterize the structure of a network; while the node-level metrics such as degree, closeness, betweenness, eigencentrality, and eccentricity are used to describe the characteristics of each node in the network. In this section, we explain those measurements in the health-care worker networks.
Diameter. The diameter is defined as the number of steps in the longest path in the network. There are two types of paths for any two nodes in the network. The first one is the shortest path, which is defined as the smallest number of steps between the two nodes, and the other one is the longest path, which has the largest number of steps between the two nodes. The network diameter is the number of steps between the two nodes, who have the largest number of steps in their longest path. Given two networks of health-care workers, if the diameter of the first one is larger than the second, then the information sharing and dissemination among health-care workers in the first network requires more steps.
Density and cohesion. Graph density is defined as the total number of edges within the network, divided by the number of edges that could exist. The cohesion of a network is described by the diameter and the average path length. The average path length is the average of the steps between all the nodes in the network. The low diameter or low average path length indicates a cohesive network with little clustering. Usually, when density increases, the average path length decreases because high-density network provides many paths along which to connect nodes. Studies show the relationship between density and average path length is nonlinear . Density values above 0.5 indicate networks have many redundant paths between nodes, and it is hard to identify structures of networks . If density values are very low, then there will be no network structures. To learn structures of health-care worker collaboration networks, we may need to prune the networks by using density values (e.g., <0.5). For instance, we can filter edges whose weight strength is low to decrease the density values of networks.
Core-periphery. Core-periphery structures are networks in which there is a group of nodes that are densely connected to one another (the core) and a separate group of nodes loosely connected to the core and loosely connected to each other (the periphery). It is not uncommon to find core-periphery networks in the health-care domain. In the NICU, nurses, neonatologists, and anesthesiologists work in a core network . In contrast, otolaryngology residents, endocrinology physicians, and hematology physicians collaborate in a periphery network .
Centralization. The typical calculation of centralization is as: , where is the centrality score (e.g., degree, betweenness, and closeness) of a node in the network, and n is the total number of nodes in the network . In the centralized network, one or a few people hold a position of power and control in the network. An alternative way to calculate centralization is to measure the standard deviation of the node centrality scores. A large standard deviation indicates a lot of variation in the individual centrality scores, and hence a centralized structure. In contrast, a small standard deviation suggests little variation and hence a decentralized structure. In a network of health-care workers, if we can identify workers with high centrality scores in the centralized network, then we can further investigate how those workers share and disseminate information in the EHR systems. Do they act like broadcasters to reach many health-care workers quickly, or do they act as gatekeepers to slow down the information sharing and dissemination?
Clustering coefficient. A clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. The metric can be defined at the network- and health-care worker levels. The network-level clustering coefficient gives an overall indication of the clustering in the network. The network-level clustering coefficient is measured as: , where a triplet is three nodes that are connected by either two (open triplet) or three (closed triplet) undirected edges. If a health-care worker network has a high clustering coefficient, then health-care workers are connected in dense pockets of interconnectivity. There are two types of network structures that connect clustered subgroups. The first is the bridge structure, in which the clustered subgroups are connected by bridges (intermediates), and the second one is the centralized structure, in which central health-care workers connect the subgroups.
Reciprocity. Reciprocity is used to characterize the symmetry in relationships between health-care workers. In network science, the reciprocity is measured in direct networks. A typical approach  to calculate the network reciprocity is: where is one if a link from i to j exists, and 0, otherwise. where n is the number of health-care workers in the network. If a network has a higher value of reciprocity, then the greater likelihood of health-care workers to be mutually linked in information receiving and dissemination in the network.
K-core. The K-core is a subset of the network, in which each health-care worker within the K-core is connected to at least K other workers. A health-care worker in the K-core sub-network is considered as one of the cores in the whole network.
Degree. The degree of a health-care worker is the total number of edges connected. The weighted degree is the sum of the weights of connected edges. In the health-care worker network, the weight of an edge can be the strength of the relationship.
Clustering coefficient. The clustering coefficient of a health-care worker is the proportion of connections among their adjacent health-care workers divided by the number of connections that could possibly exist between them. One can think of the clustering coefficient as quantification of how close a health-care worker’s neighbors are to be a clique of clinicians (e.g., a small group of clinicians, with shared interests in common patients). A health-care worker with a large clustering coefficient is the one who shares patients with health-care workers who also share patients with each other .
Betweenness. The betweenness is defined as the number of shortest paths between two health-care workers that pass through the specific health-care worker. Betweenness refers to whether a health-care worker lies on the path of others who are not directly connected. A health-care worker with a broad skillset could frequently be in a high-betweenness position. For instance, in Figure 8, clinicians 2, 3, and 4 have the largest number of shortest paths going through them. Betweenness reflects a health-care worker’s access to diverse communication channels about evidence-based practice. A high betweenness worker cares for a wide spectrum of patients.
Eigencentrality. Eigencentrality is used to quantify the influence or leadership of a health-care worker on the collaboration and coordination among health-care workers in the network. A health-care worker with a high eigencentrality is connected to workers who have high eigencentrality. An example of health-care workers with high eigencentrality is shown in Figure 9. A high eigencentrality health-care worker acts as a leader in the sharing of patients in the network.
Most of the research studies in health care are hypothesis-driven. One of the goals of the network analysis in health care is to provide evidence on network structure to assist in the designing and development of teamwork-based hypotheses. Various hypotheses can be developed between sociometric measurements and clinical outcomes, including delayed ICU admission, ICU readmission, medication error, adverse event, length of hospital stay (LOS), mortality risk, and health-care cost.
Structures of teamwork among health-care workers can be quantified by using both network- and node-level sociometric measurements. It has been recognized that structures of teamwork are associated with clinical outcomes. To inform actionable staffing interventions, we can develop hypotheses for each of the sociometric measurements and validate their relationships with clinical outcomes. For each inpatient stay (ranging from their admission to discharge), we can create a network to describe the structure of teamwork among health-care workers during the patient stay. Hypotheses can be designed based on the network. An example of the hypotheses can be: the clustering coefficient of a network is associated with LOS. Statistical models can be leveraged to test the hypotheses. The distributions of most network measurements are not Gaussian distributed, so we can use rank-based approaches to measure associations between the measurements and clinical outcomes. For instance, we can use the Spearman rank-order correlation to measure the association between the clustering coefficient and LOS. If we want to investigate multiple sociometric measurements or add confounding factors (patient demographics, the severity of sickness), we can use advanced statistical models, such as a proportional-odds (PO) logistic regression model.
The PO model can be thought of as a set of logistic regression models, where each model describes the log-odds of LOS (continuous variable) being higher than some threshold j (rather than lower than or equal to), and where j = 1, 2, …, J represents all possible thresholds by which LOS can be dichotomized, and J is equal to the number of unique outcome values minus one. The set of models is collapsed into a single model, via the proportional odds assumption that coefficients for predictor variables are the same across the threshold values. Even when this assumption is not met, a coefficient from the proportional odds model can be thought of as a weighted average of coefficients across all the threshold-specific logistic regression models.
Some outcomes, such as ICU readmission, delayed ICU admission, or mortality risk are categorical variables. In that case, we can use the Mann Whitney U test or analysis of variance (ANOVA) to test the differences in the sociometric measurements between networks. The hypotheses can also be developed between node-level measurements (e.g., betweenness, eigencentrality, and degree) and clinical outcomes. For instance, critically ill patients who were cared for by more high-betweenness nurses were significantly less likely to die in their ICU stays.
Analyzing changes in the collaboration network structures and measuring relationships of the changes with outcomes are very important research questions in the teamwork in health care. When a health-care organization adopts a new staffing intervention (e.g., creating a new team scheduling), they will need to assess and monitor the changes in the behavior of collaboration among health-care workers before and after the interventions and how such changes impact clinical outcomes. Getting feedback from the adoption of a new staffing intervention can provide evidence to identify weak and ineffective parts to do further optimization. For instance, ICUs adopt staffing interventions in the COVID-19 pandemic, and the responses may change the structure of collaboration. We can use network analysis approaches to analyze the changes in the network structures from pre-COVID-19 to intra-COVID-19 and measure the relationships of such changes with clinical outcomes such as ICU readmission or delayed ICU admission. Examples of hypotheses can be: neonatology physicians have higher betweenness after the staffing intervention or the health-care worker network after the staffing intervention has a larger diameter (the difficulty of sharing patients increases). Since sociometric measurements are not Gaussian distributed in many situations, we can apply a Mann-Whitney U test to measure the significance of the difference.
We introduce three applications to show how we use network analysis to identify care teams in a health-care organization, measure associations between collaborations and length of stay in the trauma setting, and assess health-care worker networks for the management of surgical neonates, respectively.
We applied network analysis to the EHR utilization records of over 10,000 hospital employees and 17,000 inpatients at a large academic medical center during a 4-month window . The study aimed to learn collaboration structure across the entire health-care system, and thus it built networks of departments (higher level) rather than the networks of health-care workers. Each node in the network is a department. LDA models were used to cluster patients into groups. As shown in Figures 5 and 6, matrix multiplications were conducted to transform the matrix of health-care workers and patients into the matrix of departments and patient groups. Connections among 317 departments were inferred from the department-patient group matrix. We identified 34 collaborative groups of departments . Each of the groups is a subnetwork and could be considered as a care team across various types of departments. The results suggested that, although the over 17,000 patients exhibited over 1400 different types of phenotypes, the health-care workers treating them tended to work in only 34 collaborative groups. When the 34 groups were presented to health-care experts via online surveys, 27 (79.4%) of 34 were confirmed as administratively plausible. Of those, 26 teams depicted strong collaborations, with a clustering coefficient >0.5.
We started the network analysis of trauma team structures by creating a matrix of ~5000 health-care workers and EHRs of ~5500 patients based on the EHR system utilization data . The difference is we applied a spectral co-clustering methodology to the matrix to infer groups of patients and clusters of health-care workers simultaneously. By using the co-clustering algorithm, we created three trauma patient groups, each of which has a corresponding network of health-care workers. For each network of health-care workers, we calculated sociometric measurements to quantify their structures. Length of stay was used as the outcome. The association between a sociometric measurement (e.g., clustering coefficient) and length of stay was measured by using statistical models incorporating various confounding factors (e.g., demographics and admission dates). We found a remarkably clear distinction in LOS: those patients experiencing the largest quantity of collaborations between health-care workers had the shortest LOS, while those subject to fewer collaborations (i.e., supported by less well-integrated care teams), spent much longer in hospital, indicating greater financial cost as well, of course, as pain, distress, and inconvenience to the patient .
We extracted EHR data of 15 NICU gastrostomy patients from the day prior to the patient’s surgery day until postoperative day 30. The study aims to validate the associations between health-care worker networks and post-surgical length of stay (PLOS) . For each patient ICU stay, we built a directed network to show how information was received and disseminated among health-care workers in the NICU. For each patient’s stay, we created a simplified sequence dataset by ordering health-care worker actions based on their time stamps starting from the day prior to the patient’s surgery until postoperative day 30 or the patient’s discharge date. Based on the sequences, we identified connections between health-care workers whenever their actions occurred consecutively. We learned 15 patient-level health-care worker networks. We used the sociometric measurements, including in-degree, out-degree, and betweenness, to quantify the structures of each patient-level network.
We modeled patient PSLOS with each structure measurement controlling for patient age and weight using a proportional-odds logistic regression model. Study results show health-care workers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those, who treated higher PSLOS patients (P = 0.0294). Our results demonstrate in the NICU that improved dissemination of information may be linked to reduced PSLOS.
This chapter provides an introduction of a network analysis of secondary EHR system utilization data to learn health-care worker networks. We introduce five main components when applying network analysis to team structures and clinical outcomes: (i) matrix multiplication to build connection among health-care workers, (ii) survey instruments to validate the plausibility of the learned connections among health-care workers, (iii) sociometric measurements to characterize network structures, (iv) hypothesis development to connect network structures with clinical outcomes, and (v) statistical models to test the hypotheses. Finally, we use three examples to show the application of network analysis in health care. In short, EHR data provide an efficient, accessible, and resource-friendly way to study teamwork using network analysis tools.
The research studies introduced in this chapter were supported, in part, by the National Library of Medicine of the National Institutes of Health under Award Numbers K99LM011933, R00LM011933, and R01LM012854.
The authors declare no conflict of interest.